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  "cells": [
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      "metadata": {
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      },
      "source": [
        "# EECS 498-007/598-005 Assignment 3-1: Fully-Connected Neural Networks and Dropout\n",
        "\n",
        "Before we start, please put your name and UMID in following format\n",
        "\n",
        ": Firstname LASTNAME, #00000000   //   e.g.) Justin JOHNSON, #12345678"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZeH0OvuEe1CN",
        "tags": [
          "pdf-title"
        ]
      },
      "source": [
        "# Fully-connected neural networks\n",
        "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a **single monolithic function**. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Qiu9_4pe1CP",
        "tags": [
          "pdf-ignore"
        ]
      },
      "source": [
        "In this exercise we will implement fully-connected networks using a more **modular** approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive **inputs, weights, and other parameters** and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
        "\n",
        "```python\n",
        "def forward(x, w):\n",
        "  \"\"\" Receive inputs x and weights w \"\"\"\n",
        "  # Do some computations ...\n",
        "  z = # ... some intermediate value\n",
        "  # Do some more computations ...\n",
        "  out = # the output\n",
        "   \n",
        "  cache = (x, w, z, out) # Values we need to compute gradients\n",
        "   \n",
        "  return out, cache\n",
        "```\n",
        "\n",
        "The backward pass will receive **upstream derivatives** and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
        "\n",
        "```python\n",
        "def backward(dout, cache):\n",
        "  \"\"\"\n",
        "  Receive dout (derivative of loss with respect to outputs) and cache,\n",
        "  and compute derivative with respect to inputs.\n",
        "  \"\"\"\n",
        "  # Unpack cache values\n",
        "  x, w, z, out = cache\n",
        "  \n",
        "  # Use values in cache to compute derivatives\n",
        "  dx = # Derivative of loss with respect to x\n",
        "  dw = # Derivative of loss with respect to w\n",
        "  \n",
        "  return dx, dw\n",
        "```\n",
        "\n",
        "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
        "\n",
        "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer as a tool to more efficiently optimize deep networks.\n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubB_0e-UAOVK"
      },
      "source": [
        "## Install starter code\n",
        "We will continue using the utility functions that we've used for Assignment 1 and 2: [`coutils` package](https://github.com/deepvision-class/starter-code). Run this cell to download and install it.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASkY27ZtA7Is",
        "outputId": "db092db2-6fbe-4489-91b3-f924114dbc16",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install git+https://github.com/deepvision-class/starter-code"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/deepvision-class/starter-code\n",
            "  Cloning https://github.com/deepvision-class/starter-code to /tmp/pip-req-build-r_croop1\n",
            "  Running command git clone -q https://github.com/deepvision-class/starter-code /tmp/pip-req-build-r_croop1\n",
            "Requirement already satisfied: pydrive in /usr/local/lib/python3.6/dist-packages (from Colab-Utils==0.1.dev0) (1.3.1)\n",
            "Requirement already satisfied: google-api-python-client>=1.2 in /usr/local/lib/python3.6/dist-packages (from pydrive->Colab-Utils==0.1.dev0) (1.7.12)\n",
            "Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pydrive->Colab-Utils==0.1.dev0) (4.1.3)\n",
            "Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.6/dist-packages (from pydrive->Colab-Utils==0.1.dev0) (3.13)\n",
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            "Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (0.0.4)\n",
            "Requirement already satisfied: google-auth>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (1.17.2)\n",
            "Requirement already satisfied: httplib2<1dev,>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (0.17.4)\n",
            "Requirement already satisfied: six<2dev,>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (1.15.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive->Colab-Utils==0.1.dev0) (0.2.8)\n",
            "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive->Colab-Utils==0.1.dev0) (0.4.8)\n",
            "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive->Colab-Utils==0.1.dev0) (4.6)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth>=1.4.1->google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (4.1.1)\n",
            "Requirement already satisfied: setuptools>=40.3.0 in /usr/local/lib/python3.6/dist-packages (from google-auth>=1.4.1->google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (50.3.0)\n",
            "Building wheels for collected packages: Colab-Utils\n",
            "  Building wheel for Colab-Utils (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for Colab-Utils: filename=Colab_Utils-0.1.dev0-cp36-none-any.whl size=10324 sha256=041d89c747371cd773ff7dbf2f387d96bfc57ac40dd7a72d36f1bf11e69aa7be\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-zj8jw8pt/wheels/63/d1/27/a208931527abb98d326d00209f46c80c9d745851d6a1defd10\n",
            "Successfully built Colab-Utils\n",
            "Installing collected packages: Colab-Utils\n",
            "Successfully installed Colab-Utils-0.1.dev0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzqbYcKdz6ew"
      },
      "source": [
        "## Setup code\n",
        "Run some setup code for this notebook: Import some useful packages and increase the default figure size."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HzRdJ3uhe1CR",
        "tags": [
          "pdf-ignore"
        ]
      },
      "source": [
        "import math\n",
        "import torch\n",
        "import coutils\n",
        "from coutils import fix_random_seed, rel_error, compute_numeric_gradient, Solver\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# for plotting\n",
        "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
        "plt.rcParams['image.interpolation'] = 'nearest'\n",
        "plt.rcParams['image.cmap'] = 'gray'\n",
        "\n",
        "# data type and device for torch.tensor\n",
        "to_float = {'dtype': torch.float, 'device': 'cpu'}\n",
        "to_float_cuda = {'dtype': torch.float, 'device': 'cuda'}\n",
        "to_double = {'dtype': torch.double, 'device': 'cpu'}\n",
        "to_double_cuda = {'dtype': torch.double, 'device': 'cuda'}\n",
        "to_long = {'dtype': torch.long, 'device': 'cpu'}\n",
        "to_long_cuda = {'dtype': torch.long, 'device': 'cuda'}"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xK-SJIqLDRaa"
      },
      "source": [
        "## Load CIFAR-10 data\n",
        "Here we provide a function to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npVJoyXGX2ql"
      },
      "source": [
        "def get_CIFAR10_data(validation_ratio=0.05, cuda=False, reshape_to_2d=False,\n",
        "                     visualize=False):\n",
        "  \"\"\"\n",
        "  Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
        "  it for the linear classifier. These are the same steps as we used for the\n",
        "  SVM, but condensed to a single function.  \n",
        "  \"\"\"\n",
        "  X_train, y_train, X_test, y_test = coutils.data.cifar10()\n",
        "\n",
        "  # Load every data on cuda\n",
        "  if cuda:\n",
        "    X_train = X_train.cuda()\n",
        "    y_train = y_train.cuda()\n",
        "    X_test = X_test.cuda()\n",
        "    y_test = y_test.cuda()\n",
        "\n",
        "  # 0. Visualize some examples from the dataset.\n",
        "  class_names = [\n",
        "      'plane', 'car', 'bird', 'cat', 'deer',\n",
        "      'dog', 'frog', 'horse', 'ship', 'truck'\n",
        "  ]\n",
        "  if visualize:\n",
        "    img = coutils.utils.visualize_dataset(X_train, y_train, 12, class_names)\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.imshow(img)\n",
        "    plt.title('Original Examples')\n",
        "    plt.axis('off')\n",
        "    # plt.show()\n",
        "\n",
        "  # 1. Normalize the data: subtract the mean RGB (zero mean)\n",
        "  mean_image = X_train.mean(dim=0, keepdim=True).mean(dim=2, keepdim=True).mean(dim=3, keepdim=True)\n",
        "  X_train -= mean_image\n",
        "  X_test -= mean_image\n",
        "  if visualize:\n",
        "    img1 = coutils.utils.visualize_dataset(X_train, y_train, 12, class_names)\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.imshow(img1)\n",
        "    plt.axis('off')\n",
        "    plt.title('Examples after Normalization')\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "  # 2. Reshape the image data into rows\n",
        "  if reshape_to_2d:\n",
        "    X_train = X_train.reshape(X_train.shape[0], -1)\n",
        "    X_test = X_test.reshape(X_test.shape[0], -1)\n",
        "\n",
        "  # 3. Take the validation set from the training set\n",
        "  # Note: It should not be taken from the test set\n",
        "  # For random permumation, you can use torch.randperm or torch.randint\n",
        "  # But, for this homework, we use slicing instead.\n",
        "  num_training = int( X_train.shape[0] * (1.0 - validation_ratio) )\n",
        "  num_validation = X_train.shape[0] - num_training\n",
        "\n",
        "  # return the dataset\n",
        "  data_dict = {}\n",
        "  data_dict['X_val'] = X_train[num_training:num_training + num_validation]\n",
        "  data_dict['y_val'] = y_train[num_training:num_training + num_validation]\n",
        "  data_dict['X_train'] = X_train[0:num_training]\n",
        "  data_dict['y_train'] = y_train[0:num_training]\n",
        "\n",
        "  data_dict['X_test'] = X_test\n",
        "  data_dict['y_test'] = y_test\n",
        "  return data_dict"
      ],
      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "# Invoke the above function to get our data.\n",
        "data_dict = get_CIFAR10_data(visualize=True)\n",
        "print('Train data shape: ', data_dict['X_train'].shape)\n",
        "print('Train labels shape: ', data_dict['y_train'].shape)\n",
        "print('Validation data shape: ', data_dict['X_val'].shape)\n",
        "print('Validation labels shape: ', data_dict['y_val'].shape)\n",
        "print('Test data shape: ', data_dict['X_test'].shape)\n",
        "print('Test labels shape: ', data_dict['y_test'].shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e9a0a1d7894143df95c9b018bae7b85f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./cifar-10-python.tar.gz to .\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/coutils/utils.py:54: UserWarning: This overload of nonzero is deprecated:\n",
            "\tnonzero()\n",
            "Consider using one of the following signatures instead:\n",
            "\tnonzero(*, bool as_tuple) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.)\n",
            "  idxs = (y_data == y).nonzero().view(-1)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Train data shape:  torch.Size([47500, 3, 32, 32])\n",
            "Train labels shape:  torch.Size([47500])\n",
            "Validation data shape:  torch.Size([2500, 3, 32, 32])\n",
            "Validation labels shape:  torch.Size([2500])\n",
            "Test data shape:  torch.Size([10000, 3, 32, 32])\n",
            "Test labels shape:  torch.Size([10000])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JB7Eu3qJ9xnm"
      },
      "source": [
        "# Linear layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bRdnxsvZunFu"
      },
      "source": [
        "For each layer we implement, we will define a class with two static methods `forward` and `backward`.\n",
        "\n",
        "For now the `forward` and `backward` methods are stubs. We will actually implement them in the following cells."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ZCEVswF96aq"
      },
      "source": [
        "class Linear(object):\n",
        "\n",
        "  @staticmethod\n",
        "  def forward(x, w, b):\n",
        "    raise NotImplementedError\n",
        "\n",
        "  @staticmethod\n",
        "  def backward(dout, cache):\n",
        "    raise NotImplementedError"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0NNv3l-ne1Cb"
      },
      "source": [
        "## Linear layer: forward\n",
        "Implement the `Linear.forward` function. Once you are done you can test your implementaion by running the next cell:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vc-qQp11axc0"
      },
      "source": [
        "def linear_forward(x, w, b):\n",
        "  \"\"\"\n",
        "  Computes the forward pass for an linear (fully-connected) layer.\n",
        "  The input x has shape (N, d_1, ..., d_k) and contains a minibatch of N\n",
        "  examples, where each example x[i] has shape (d_1, ..., d_k). We will\n",
        "  reshape each input into a vector of dimension D = d_1 * ... * d_k, and\n",
        "  then transform it to an output vector of dimension M.\n",
        "  Inputs:\n",
        "  - x: A tensor containing input data, of shape (N, d_1, ..., d_k)\n",
        "  - w: A tensor of weights, of shape (D, M)\n",
        "  - b: A tensor of biases, of shape (M,)\n",
        "  Returns a tuple of:\n",
        "  - out: output, of shape (N, M)\n",
        "  - cache: (x, w, b)\n",
        "  \"\"\"\n",
        "  out = None\n",
        "  #############################################################################\n",
        "  # TODO: Implement the linear forward pass. Store the result in out. You     #\n",
        "  # will need to reshape the input into rows.                                 #\n",
        "  #############################################################################\n",
        "  # Replace \"pass\" statement with your code\n",
        "  out = x.reshape(-1, w.shape[0]).mm(w) + b  # N x M\n",
        "  #############################################################################\n",
        "  #                              END OF YOUR CODE                             #\n",
        "  #############################################################################\n",
        "  cache = (x, w, b)\n",
        "  return out, cache\n",
        "\n",
        "Linear.forward = linear_forward"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8_B1QwbuHeq"
      },
      "source": [
        "Test the `Linear.forward` function. You should see errors less than `1e-8`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjq2Sq4Ze1Cc",
        "outputId": "a82d753a-16f0-41c5-bc1c-1d1aa16a407b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Test the Linear.forward function\n",
        "num_inputs = 2\n",
        "input_shape = torch.tensor((4, 5, 6))\n",
        "output_dim = 3\n",
        "\n",
        "input_size = num_inputs * torch.prod(input_shape)\n",
        "weight_size = output_dim * torch.prod(input_shape)\n",
        "\n",
        "x = torch.linspace(-0.1, 0.5, steps=input_size, **to_double_cuda).reshape(num_inputs, *input_shape)\n",
        "w = torch.linspace(-0.2, 0.3, steps=weight_size, **to_double_cuda).reshape(torch.prod(input_shape), output_dim)\n",
        "b = torch.linspace(-0.3, 0.1, steps=output_dim, **to_double_cuda)\n",
        "\n",
        "out, _ = Linear.forward(x, w, b)\n",
        "correct_out = torch.tensor([[ 1.49834967,  1.70660132,  1.91485297],\n",
        "                            [ 3.25553199,  3.5141327,   3.77273342]],\n",
        "                            **to_double_cuda)\n",
        "\n",
        "print('Testing Linear.forward function:')\n",
        "print('difference: ', rel_error(out, correct_out))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing Linear.forward function:\n",
            "difference:  1.9539697795885023e-09\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4mxIDo46e1Cf"
      },
      "source": [
        "## Linear layer: backward\n",
        "Now implement the `Linear.backward` function and test your implementation using numeric gradient checking."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7emXOqm7fO7T"
      },
      "source": [
        "def linear_backward(dout, cache):\n",
        "  \"\"\"\n",
        "  Computes the backward pass for an linear layer.\n",
        "  Inputs:\n",
        "  - dout: Upstream derivative, of shape (N, M)\n",
        "  - cache: Tuple of:\n",
        "    - x: Input data, of shape (N, d_1, ... d_k)\n",
        "    - w: Weights, of shape (D, M)\n",
        "    - b: Biases, of shape (M,)\n",
        "  Returns a tuple of:\n",
        "  - dx: Gradient with respect to x, of shape (N, d1, ..., d_k)\n",
        "  - dw: Gradient with respect to w, of shape (D, M)\n",
        "  - db: Gradient with respect to b, of shape (M,)\n",
        "  \"\"\"\n",
        "  x, w, b = cache\n",
        "  dx, dw, db = None, None, None\n",
        "  #############################################################################\n",
        "  # TODO: Implement the linear backward pass.                                 #\n",
        "  #############################################################################\n",
        "  # Replace \"pass\" statement with your code\n",
        "  dx = dout.mm(w.t()).reshape(x.shape)\n",
        "  dw = x.view(x.shape[0], -1).t().mm(dout)\n",
        "  db = dout.sum(dim=0)\n",
        "  #############################################################################\n",
        "  #                              END OF YOUR CODE                             #\n",
        "  #############################################################################\n",
        "  return dx, dw, db\n",
        "\n",
        "Linear.backward = linear_backward"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aoJQSun1vJCa"
      },
      "source": [
        "Run the following to test your implementation of `Linear.backward`. You should see errors less than `1e-8`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ts85gmote1Cg",
        "outputId": "26b587c9-c598-4d52-d300-41f750e5a5f2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Test the Linear.backward function\n",
        "fix_random_seed(0)\n",
        "x = torch.randn(10, 2, 3, **to_double_cuda)\n",
        "w = torch.randn(6, 5, **to_double_cuda)\n",
        "b = torch.randn(5, **to_double_cuda)\n",
        "dout = torch.randn(10, 5, **to_double_cuda)\n",
        "\n",
        "dx_num = compute_numeric_gradient(lambda x: Linear.forward(x, w, b)[0], x, dout)\n",
        "dw_num = compute_numeric_gradient(lambda w: Linear.forward(x, w, b)[0], w, dout)\n",
        "db_num = compute_numeric_gradient(lambda b: Linear.forward(x, w, b)[0], b, dout)\n",
        "\n",
        "_, cache = Linear.forward(x, w, b)\n",
        "dx, dw, db = Linear.backward(dout, cache)\n",
        "\n",
        "# The error should be around e-10 or less\n",
        "print('Testing Linear.backward function:')\n",
        "print('dx error: ', rel_error(dx_num, dx))\n",
        "print('dw error: ', rel_error(dw_num, dw))\n",
        "print('db error: ', rel_error(db_num, db))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing Linear.backward function:\n",
            "dx error:  2.340771536181963e-09\n",
            "dw error:  6.573349127694478e-11\n",
            "db error:  4.357966842062988e-11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bdIqQzqiJQE6"
      },
      "source": [
        "# ReLU activation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YdX98A_qvTRt"
      },
      "source": [
        "We will now implement the ReLU nonlinearity. As above, we will define a class with two empty static methods, and implement them in upcoming cells."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZ4d9xi5JZ4h"
      },
      "source": [
        "class ReLU(object):\n",
        "\n",
        "  @staticmethod\n",
        "  def forward(x, w, b):\n",
        "    raise NotImplementedError\n",
        "\n",
        "  @staticmethod\n",
        "  def backward(dout, cache):\n",
        "    raise NotImplementedError"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n2DyqL4Ae1Cl"
      },
      "source": [
        "## ReLU activation: forward\n",
        "Implement the forward pass for the ReLU activation function in the `ReLU.forward` function.\n",
        "\n",
        "You should not change the input tensor with an in-place operation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "suy8VIfUxSTZ"
      },
      "source": [
        "def relu_forward(x):\n",
        "  \"\"\"\n",
        "  Computes the forward pass for a layer of rectified linear units (ReLUs).\n",
        "  Input:\n",
        "  - x: Input; a tensor of any shape\n",
        "  Returns a tuple of:\n",
        "  - out: Output, a tensor of the same shape as x\n",
        "  - cache: x\n",
        "  \"\"\"\n",
        "  out = None\n",
        "  #############################################################################\n",
        "  # TODO: Implement the ReLU forward pass.                                    #\n",
        "  # You should not change the input tensor with an in-place operation.        #\n",
        "  #############################################################################\n",
        "  # Replace \"pass\" statement with your code\n",
        "  out = torch.max(torch.tensor([0.0], dtype=x.dtype).cuda(), x)\n",
        "  #############################################################################\n",
        "  #                              END OF YOUR CODE                             #\n",
        "  #############################################################################\n",
        "  cache = x\n",
        "  return out, cache\n",
        "\n",
        "ReLU.forward = relu_forward"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9XGN5pd0wyrg"
      },
      "source": [
        "Run the following to test your implementation of the ReLU forward pass. Your errors should be less than `1e-7`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QblpieUJe1Cm",
        "outputId": "14a42b8a-180c-45cf-9d2e-50e0f2196500",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Test the relu_forward function\n",
        "x = torch.linspace(-0.5, 0.5, steps=12, **to_double_cuda).reshape(3, 4)\n",
        "\n",
        "out, _ = ReLU.forward(x)\n",
        "correct_out = torch.tensor([[ 0.,          0.,          0.,          0.,        ],\n",
        "                            [ 0.,          0.,          0.04545455,  0.13636364,],\n",
        "                            [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]],\n",
        "                            **to_double_cuda)\n",
        "\n",
        "# Compare your output with ours. The error should be on the order of e-8\n",
        "print('Testing ReLU.forward function:')\n",
        "print('difference: ', rel_error(out, correct_out))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing ReLU.forward function:\n",
            "difference:  9.999999034982122e-08\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bSInb7xe1Cq"
      },
      "source": [
        "## ReLU activation: backward\n",
        "Now implement the backward pass for the ReLU activation function.\n",
        "\n",
        "Again, you should not change the input tensor with an in-place operation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVi2u1d9I1gi",
        "outputId": "a6d32495-3ee5-4775-c229-e3a5276c8849",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x = torch.randn(10, 10, **to_double_cuda)\n",
        "y = torch.zeros(1, dtype=x.dtype, device=x.device)\n",
        "z = torch.ones(1, dtype=x.dtype, device=x.device)\n",
        "torch.where(x <= 0, y, z)\n",
        "a = torch.tensor([[1,2],[3,4]])\n",
        "b = torch.tensor([[1,2],[3,4]])\n",
        "print(a.shape)\n",
        "print(a[None].shape)\n",
        "a.clamp(min=0,max=1)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([2, 2])\n",
            "torch.Size([1, 2, 2])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 1],\n",
              "        [1, 1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWffiu_dyDhr"
      },
      "source": [
        "def relu_backward(dout, cache):\n",
        "  \"\"\"\n",
        "  Computes the backward pass for a layer of rectified linear units (ReLUs).\n",
        "  Input:\n",
        "  - dout: Upstream derivatives, of any shape\n",
        "  - cache: Input x, of same shape as dout\n",
        "  Returns:\n",
        "  - dx: Gradient with respect to x\n",
        "  \"\"\"\n",
        "  dx, x = None, cache\n",
        "  #############################################################################\n",
        "  # TODO: Implement the ReLU backward pass.                                   #\n",
        "  # You should not change the input tensor with an in-place operation.        #\n",
        "  # Hint: torch.clamp doesn't work\n",
        "  #############################################################################\n",
        "  # Replace \"pass\" statement with your code\n",
        "  one = torch.ones(1, dtype=x.dtype, device=x.device)\n",
        "  zero = torch.zeros(1, dtype=x.dtype, device=x.device)\n",
        "  dx = torch.where(x > 0, one, zero) * dout\n",
        "  #############################################################################\n",
        "  #                              END OF YOUR CODE                             #\n",
        "  #############################################################################\n",
        "  return dx\n",
        "\n",
        "ReLU.backward = relu_backward"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KKRsZPVHxZuz"
      },
      "source": [
        "Run the following to test your implementation of `ReLU.backward`. Your errors should be less than `1e-10`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odiV48zBe1Cr",
        "outputId": "419f2c60-8394-41d9-e6ad-97d171529969",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "fix_random_seed(0)\n",
        "x = torch.randn(10, 10, **to_double_cuda)\n",
        "dout = torch.randn(*x.shape, **to_double_cuda)\n",
        "\n",
        "dx_num = compute_numeric_gradient(lambda x: ReLU.forward(x)[0], x, dout)\n",
        "\n",
        "_, cache = ReLU.forward(x)\n",
        "dx = ReLU.backward(dout, cache)\n",
        "\n",
        "# The error should be on the order of e-12\n",
        "print('Testing ReLU.backward function:')\n",
        "print('dx error: ', rel_error(dx_num, dx))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing ReLU.backward function:\n",
            "dx error:  6.5510464861138194e-12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVTMuUOZe1Cv"
      },
      "source": [
        "# \"Sandwich\" layers\n",
        "There are some common patterns of layers that are frequently used in neural nets. For example, linear layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define a convenience layer.\n",
        "\n",
        "This also shows how our layer abstraction allows us to implement new layers by composing existing layer implementations. This is a powerful mechanism for structuring deep learning code in a modular fashion.\n",
        "\n",
        "For now take a look at the `forward` and `backward` functions in `Linear_ReLU`, and run the following to numerically gradient check the backward pass:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L1a6zFSQMOOo"
      },
      "source": [
        "class Linear_ReLU(object):\n",
        "\n",
        "  @staticmethod\n",
        "  def forward(x, w, b):\n",
        "    \"\"\"\n",
        "    Convenience layer that perorms an linear transform followed by a ReLU.\n",
        "\n",
        "    Inputs:\n",
        "    - x: Input to the linear layer\n",
        "    - w, b: Weights for the linear layer\n",
        "    Returns a tuple of:\n",
        "    - out: Output from the ReLU\n",
        "    - cache: Object to give to the backward pass\n",
        "    \"\"\"\n",
        "    a, fc_cache = Linear.forward(x, w, b)\n",
        "    out, relu_cache = ReLU.forward(a)\n",
        "    cache = (fc_cache, relu_cache)\n",
        "    return out, cache\n",
        "\n",
        "  @staticmethod\n",
        "  def backward(dout, cache):\n",
        "    \"\"\"\n",
        "    Backward pass for the linear-relu convenience layer\n",
        "    \"\"\"\n",
        "    fc_cache, relu_cache = cache\n",
        "    da = ReLU.backward(dout, relu_cache)\n",
        "    dx, dw, db = Linear.backward(da, fc_cache)\n",
        "    return dx, dw, db"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PnnnLp_pyN0W"
      },
      "source": [
        "Run the following to test the implementation of the `Linear_ReLU` layer using numeric gradient checking. You should see errors less than `1e-8`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gaY5YfAe1Cw",
        "outputId": "95af93eb-5a76-49f2-93bb-55d942aa1065",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "fix_random_seed(0)\n",
        "x = torch.randn(2, 3, 4, **to_double_cuda)\n",
        "w = torch.randn(12, 10, **to_double_cuda)\n",
        "b = torch.randn(10, **to_double_cuda)\n",
        "dout = torch.randn(2, 10, **to_double_cuda)\n",
        "\n",
        "out, cache = Linear_ReLU.forward(x, w, b)\n",
        "dx, dw, db = Linear_ReLU.backward(dout, cache)\n",
        "\n",
        "dx_num = compute_numeric_gradient(lambda x: Linear_ReLU.forward(x, w, b)[0], x, dout)\n",
        "dw_num = compute_numeric_gradient(lambda w: Linear_ReLU.forward(x, w, b)[0], w, dout)\n",
        "db_num = compute_numeric_gradient(lambda b: Linear_ReLU.forward(x, w, b)[0], b, dout)\n",
        "\n",
        "# Relative error should be around e-8 or less\n",
        "print('Testing Linear_ReLU.forward and Linear_ReLU.backward:')\n",
        "print('dx error: ', rel_error(dx_num, dx))\n",
        "print('dw error: ', rel_error(dw_num, dw))\n",
        "print('db error: ', rel_error(db_num, db))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing Linear_ReLU.forward and Linear_ReLU.backward:\n",
            "dx error:  5.0562785959952e-10\n",
            "dw error:  1.8447601369756057e-09\n",
            "db error:  5.0959990317728124e-11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAGgiyP5e1C0"
      },
      "source": [
        "# Loss layers: Softmax and SVM\n",
        "**You implemented these loss functions in the last assignmen**t, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations.\n",
        "\n",
        "You can make sure that the implementations are correct by running the following:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-OG6d7RNOxj"
      },
      "source": [
        "def svm_loss(x, y):\n",
        "  \"\"\"\n",
        "  Computes the loss and gradient using for multiclass SVM classification.\n",
        "  Inputs:\n",
        "  - x: Input data, of shape (N, C) where x[i, j] is the score for the jth\n",
        "    class for the ith input.\n",
        "  - y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and\n",
        "    0 <= y[i] < C\n",
        "  Returns a tuple of:\n",
        "  - loss: Scalar giving the loss\n",
        "  - dx: Gradient of the loss with respect to x\n",
        "  \"\"\"\n",
        "  N = x.shape[0]\n",
        "  correct_class_scores = x[torch.arange(N), y]\n",
        "  # Slicing with None ?\n",
        "  margins = (x - correct_class_scores[:, None] + 1.0).clamp(min=0.)\n",
        "  margins[torch.arange(N), y] = 0.\n",
        "  loss = margins.sum() / N\n",
        "  num_pos = (margins > 0).sum(dim=1)\n",
        "  dx = torch.zeros_like(x)\n",
        "  dx[margins > 0] = 1.\n",
        "  dx[torch.arange(N), y] -= num_pos.to(dx.dtype)\n",
        "  dx /= N\n",
        "  return loss, dx\n",
        "\n",
        "\n",
        "def softmax_loss(x, y):\n",
        "  \"\"\"\n",
        "  Computes the loss and gradient for softmax classification.\n",
        "  Inputs:\n",
        "  - x: Input data, of shape (N, C) where x[i, j] is the score for the jth\n",
        "    class for the ith input.\n",
        "  - y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and\n",
        "    0 <= y[i] < C\n",
        "  Returns a tuple of:\n",
        "  - loss: Scalar giving the loss\n",
        "  - dx: Gradient of the loss with respect to x\n",
        "  \"\"\"\n",
        "  shifted_logits = x - x.max(dim=1, keepdim=True).values\n",
        "  Z = shifted_logits.exp().sum(dim=1, keepdim=True)\n",
        "  log_probs = shifted_logits - Z.log()\n",
        "  probs = log_probs.exp()\n",
        "  N = x.shape[0]\n",
        "  loss = (-1.0/ N) * log_probs[torch.arange(N), y].sum()\n",
        "  dx = probs.clone()\n",
        "  dx[torch.arange(N), y] -= 1\n",
        "  dx /= N\n",
        "  return loss, dx"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5JzILk6Q0FpO"
      },
      "source": [
        "Run the following to perform numeric gradient checking on the two loss functions. You should see errors less than `1e-7`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BU9xp64De1C1",
        "outputId": "8be200fe-bf57-4598-8366-fad6f630cd26",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "fix_random_seed(0)\n",
        "num_classes, num_inputs = 10, 50\n",
        "x = 0.001 * torch.randn(num_inputs, num_classes, **to_double_cuda)\n",
        "y = torch.randint(num_classes, size=(num_inputs,), **to_long_cuda)\n",
        "\n",
        "dx_num = compute_numeric_gradient(lambda x: svm_loss(x, y)[0], x)\n",
        "loss, dx = svm_loss(x, y)\n",
        "\n",
        "# Test svm_loss function. Loss should be around 9 and dx error should be around the order of e-9\n",
        "print('Testing svm_loss:')\n",
        "print('loss: ', loss.item())\n",
        "print('dx error: ', rel_error(dx_num, dx))\n",
        "\n",
        "dx_num = compute_numeric_gradient(lambda x: softmax_loss(x, y)[0], x)\n",
        "loss, dx = softmax_loss(x, y)\n",
        "\n",
        "# Test softmax_loss function. Loss should be close to 2.3 and dx error should be around e-8\n",
        "print('\\nTesting softmax_loss:')\n",
        "print('loss: ', loss.item())\n",
        "print('dx error: ', rel_error(dx_num, dx))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing svm_loss:\n",
            "loss:  9.000430792478463\n",
            "dx error:  2.8043130317900733e-09\n",
            "\n",
            "Testing softmax_loss:\n",
            "loss:  2.3026286102347924\n",
            "dx error:  1.9329571390449453e-08\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qq7-cyfQe1C4"
      },
      "source": [
        "# Two-layer network\n",
        "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
        "\n",
        "Complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "If6ZpLCfOLRL"
      },
      "source": [
        "class TwoLayerNet(object):\n",
        "  \"\"\"\n",
        "  A two-layer fully-connected neural network with ReLU nonlinearity and\n",
        "  softmax loss that uses a modular layer design. We assume an input dimension\n",
        "  of D, a hidden dimension of H, and perform classification over C classes.\n",
        "  The architecure should be linear - relu - linear - softmax.\n",
        "  Note that this class does not implement gradient descent; instead, it\n",
        "  will interact with a separate Solver object that is responsible for running\n",
        "  optimization.\n",
        "\n",
        "  The learnable parameters of the model are stored in the dictionary\n",
        "  self.params that maps parameter names to PyTorch tensors.\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, input_dim=3*32*32, hidden_dim=100, num_classes=10,\n",
        "         weight_scale=1e-3, reg=0.0, dtype=torch.float32, device='cpu'):\n",
        "    \"\"\"\n",
        "    Initialize a new network.\n",
        "    Inputs:\n",
        "    - input_dim: An integer giving the size of the input\n",
        "    - hidden_dim: An integer giving the size of the hidden layer\n",
        "    - num_classes: An integer giving the number of classes to classify\n",
        "    - weight_scale: Scalar giving the standard deviation for random\n",
        "      initialization of the weights.\n",
        "    - reg: Scalar giving L2 regularization strength.\n",
        "    - dtype: A torch data type object; all computations will be performed using\n",
        "      this datatype. float is faster but less accurate, so you should use\n",
        "      double for numeric gradient checking.\n",
        "    - device: device to use for computation. 'cpu' or 'cuda'\n",
        "    \"\"\"\n",
        "    self.params = {}\n",
        "    self.reg = reg\n",
        "\n",
        "    ###########################################################################\n",
        "    # TODO: Initialize the weights and biases of the two-layer net. Weights   #\n",
        "    # should be initialized from a Gaussian centered at 0.0 with              #\n",
        "    # standard deviation equal to weight_scale, and biases should be          #\n",
        "    # initialized to zero. All weights and biases should be stored in the     #\n",
        "    # dictionary self.params, with first layer weights                        #\n",
        "    # and biases using the keys 'W1' and 'b1' and second layer                #\n",
        "    # weights and biases using the keys 'W2' and 'b2'.                        #\n",
        "    ###########################################################################\n",
        "    # Replace \"pass\" statement with your code\n",
        "    self.params['W1'] = torch.randn((input_dim, hidden_dim), dtype=dtype, device=device) * weight_scale\n",
        "    self.params['b1'] = torch.zeros(hidden_dim, dtype=dtype, device=device)\n",
        "    self.params['W2'] = torch.randn((hidden_dim, num_classes), dtype=dtype, device=device) * weight_scale\n",
        "    self.params['b2'] = torch.zeros(num_classes, dtype=dtype, device=device)\n",
        "    ###########################################################################\n",
        "    #                            END OF YOUR CODE                             #\n",
        "    ###########################################################################\n",
        "\n",
        "\n",
        "  def loss(self, X, y=None):\n",
        "    \"\"\"\n",
        "    Compute loss and gradient for a minibatch of data.\n",
        "\n",
        "    Inputs:\n",
        "    - X: Tensor of input data of shape (N, d_1, ..., d_k)\n",
        "    - y: int64 Tensor of labels, of shape (N,). y[i] gives the label for X[i].\n",
        "\n",
        "    Returns:\n",
        "    If y is None, then run a test-time forward pass of the model and return:\n",
        "    - scores: Tensor of shape (N, C) giving classification scores, where\n",
        "      scores[i, c] is the classification score for X[i] and class c.\n",
        "    If y is not None, then run a training-time forward and backward pass and\n",
        "    return a tuple of:\n",
        "    - loss: Scalar value giving the loss\n",
        "    - grads: Dictionary with the same keys as self.params, mapping parameter\n",
        "      names to gradients of the loss with respect to those parameters.\n",
        "    \"\"\"\n",
        "    scores = None\n",
        "    ###########################################################################\n",
        "    # TODO: Implement the forward pass for the two-layer net, computing the   #\n",
        "    # class scores for X and storing them in the scores variable.             #\n",
        "    ###########################################################################\n",
        "    # Replace \"pass\" statement with your code\n",
        "    hidden, hidden_cache = Linear_ReLU.forward(X, self.params['W1'], self.params['b1'])\n",
        "    scores, cache = Linear.forward(hidden, self.params['W2'], self.params['b2'])\n",
        "    ###########################################################################\n",
        "    #                            END OF YOUR CODE                             #\n",
        "    ###########################################################################\n",
        "\n",
        "    # If y is None then we are in test mode so just return scores\n",
        "    if y is None:\n",
        "      return scores\n",
        "\n",
        "    loss, grads = 0, {}\n",
        "    ###########################################################################\n",
        "    # TODO: Implement the backward pass for the two-layer net. Store the loss #\n",
        "    # in the loss variable and gradients in the grads dictionary. Compute data#\n",
        "    # loss using softmax, and make sure that grads[k] holds the gradients for #\n",
        "    # self.params[k]. Don't forget to add L2 regularization!                  #\n",
        "    #                                                                         #\n",
        "    # NOTE: To ensure that your implementation matches ours and you pass the  #\n",
        "    # automated tests, make sure that your L2 regularization does not include #\n",
        "    # a factor of 0.5.                                                        #\n",
        "    ###########################################################################\n",
        "    # Replace \"pass\" statement with your code\n",
        "    loss, dout = softmax_loss(scores, y) \n",
        "    loss += self.reg * (torch.sum(self.params['W1'] ** 2) + torch.sum(self.params['W2'] ** 2))\n",
        "    d_h, grads['W2'], grads['b2'] = Linear.backward(dout, cache)\n",
        "    \n",
        "    _, grads['W1'], grads['b1'] = Linear_ReLU.backward(d_h, hidden_cache)\n",
        "\n",
        "    ###########################################################################\n",
        "    #                            END OF YOUR CODE                             #\n",
        "    ###########################################################################\n",
        "\n",
        "    return loss, grads"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bIdqEy-l0-Uu"
      },
      "source": [
        "Once you have finished implementing the forward and backward passes of your two-layer net, run the following to test your implementation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3JOcfyze1C5",
        "outputId": "4ac82c0c-758e-41a8-95ab-cff55e6f528e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "fix_random_seed(0)\n",
        "N, D, H, C = 3, 5, 50, 7\n",
        "X = torch.randn(N, D, **to_double_cuda)\n",
        "y = torch.randint(C, size=(N,), **to_long_cuda)\n",
        "\n",
        "std = 1e-3\n",
        "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std,\n",
        "                    **to_double_cuda)\n",
        "\n",
        "print('Testing initialization ... ')\n",
        "W1_std = torch.abs(model.params['W1'].std() - std)\n",
        "b1 = model.params['b1']\n",
        "W2_std = torch.abs(model.params['W2'].std() - std)\n",
        "b2 = model.params['b2']\n",
        "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
        "assert torch.all(b1 == 0), 'First layer biases do not seem right'\n",
        "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
        "assert torch.all(b2 == 0), 'Second layer biases do not seem right'\n",
        "\n",
        "print('Testing test-time forward pass ... ')\n",
        "model.params['W1'] = torch.linspace(-0.7, 0.3, steps=D*H, **to_double_cuda).reshape(D, H)\n",
        "model.params['b1'] = torch.linspace(-0.1, 0.9, steps=H, **to_double_cuda)\n",
        "model.params['W2'] = torch.linspace(-0.3, 0.4, steps=H*C, **to_double_cuda).reshape(H, C)\n",
        "model.params['b2'] = torch.linspace(-0.9, 0.1, steps=C, **to_double_cuda)\n",
        "X = torch.linspace(-5.5, 4.5, steps=N*D, **to_double_cuda).reshape(D, N).t()\n",
        "scores = model.loss(X)\n",
        "correct_scores = torch.tensor(\n",
        "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
        "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
        "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]],\n",
        "    **to_double_cuda)\n",
        "scores_diff = torch.abs(scores - correct_scores).sum()\n",
        "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
        "\n",
        "print('Testing training loss (no regularization)')\n",
        "y = torch.tensor([0, 5, 1])\n",
        "loss, grads = model.loss(X, y)\n",
        "correct_loss = 3.4702243556\n",
        "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
        "\n",
        "model.reg = 1.0\n",
        "loss, grads = model.loss(X, y)\n",
        "correct_loss = 49.719461034881775\n",
        "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
        "\n",
        "# Errors should be around e-6 or less\n",
        "for reg in [0.0, 0.7]:\n",
        "  print('Running numeric gradient check with reg = ', reg)\n",
        "  model.reg = reg\n",
        "  loss, grads = model.loss(X, y)\n",
        "\n",
        "  for name in sorted(grads):\n",
        "    f = lambda _: model.loss(X, y)[0]\n",
        "    grad_num = compute_numeric_gradient(f, model.params[name])\n",
        "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing initialization ... \n",
            "Testing test-time forward pass ... \n",
            "Testing training loss (no regularization)\n",
            "Running numeric gradient check with reg =  0.0\n",
            "W1 relative error: 4.28e-08\n",
            "W2 relative error: 7.01e-10\n",
            "b1 relative error: 2.77e-08\n",
            "b2 relative error: 6.15e-10\n",
            "Running numeric gradient check with reg =  0.7\n",
            "W1 relative error: 1.15e+02\n",
            "W2 relative error: 3.14e+00\n",
            "b1 relative error: 3.13e-08\n",
            "b2 relative error: 1.78e-09\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1Odj9XQe1C9"
      },
      "source": [
        "# Solver\n",
        "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
        "\n",
        "Read through `help(Solver)` to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZ-8wKffRoDu",
        "outputId": "a80b2509-3fc3-4bc4-f3e7-6b7655fddd0a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(help(Solver))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Help on class Solver in module coutils.solver:\n",
            "\n",
            "class Solver(builtins.object)\n",
            " |  A Solver encapsulates all the logic necessary for training classification\n",
            " |  models. The Solver performs stochastic gradient descent using different\n",
            " |  update rules.\n",
            " |  The solver accepts both training and validation data and labels so it can\n",
            " |  periodically check classification accuracy on both training and validation\n",
            " |  data to watch out for overfitting.\n",
            " |  To train a model, you will first construct a Solver instance, passing the\n",
            " |  model, dataset, and various options (learning rate, batch size, etc) to the\n",
            " |  constructor. You will then call the train() method to run the optimization\n",
            " |  procedure and train the model.\n",
            " |  After the train() method returns, model.params will contain the parameters\n",
            " |  that performed best on the validation set over the course of training.\n",
            " |  In addition, the instance variable solver.loss_history will contain a list\n",
            " |  of all losses encountered during training and the instance variables\n",
            " |  solver.train_acc_history and solver.val_acc_history will be lists of the\n",
            " |  accuracies of the model on the training and validation set at each epoch.\n",
            " |  Example usage might look something like this:\n",
            " |  data = {\n",
            " |    'X_train': # training data\n",
            " |    'y_train': # training labels\n",
            " |    'X_val': # validation data\n",
            " |    'y_val': # validation labels\n",
            " |  }\n",
            " |  model = MyAwesomeModel(hidden_size=100, reg=10)\n",
            " |  solver = Solver(model, data,\n",
            " |          update_rule=sgd,\n",
            " |          optim_config={\n",
            " |            'learning_rate': 1e-3,\n",
            " |          },\n",
            " |          lr_decay=0.95,\n",
            " |          num_epochs=10, batch_size=100,\n",
            " |          print_every=100,\n",
            " |          device='cuda')\n",
            " |  solver.train()\n",
            " |  A Solver works on a model object that must conform to the following API:\n",
            " |  - model.params must be a dictionary mapping string parameter names to numpy\n",
            " |    arrays containing parameter values.\n",
            " |  - model.loss(X, y) must be a function that computes training-time loss and\n",
            " |    gradients, and test-time classification scores, with the following inputs\n",
            " |    and outputs:\n",
            " |    Inputs:\n",
            " |    - X: Array giving a minibatch of input data of shape (N, d_1, ..., d_k)\n",
            " |    - y: Array of labels, of shape (N,) giving labels for X where y[i] is the\n",
            " |    label for X[i].\n",
            " |    Returns:\n",
            " |    If y is None, run a test-time forward pass and return:\n",
            " |    - scores: Array of shape (N, C) giving classification scores for X where\n",
            " |    scores[i, c] gives the score of class c for X[i].\n",
            " |    If y is not None, run a training time forward and backward pass and\n",
            " |    return a tuple of:\n",
            " |    - loss: Scalar giving the loss\n",
            " |    - grads: Dictionary with the same keys as self.params mapping parameter\n",
            " |    names to gradients of the loss with respect to those parameters.\n",
            " |    - device: device to use for computation. 'cpu' or 'cuda'\n",
            " |  \n",
            " |  Methods defined here:\n",
            " |  \n",
            " |  __init__(self, model, data, **kwargs)\n",
            " |      Construct a new Solver instance.\n",
            " |      Required arguments:\n",
            " |      - model: A model object conforming to the API described above\n",
            " |      - data: A dictionary of training and validation data containing:\n",
            " |        'X_train': Array, shape (N_train, d_1, ..., d_k) of training images\n",
            " |        'X_val': Array, shape (N_val, d_1, ..., d_k) of validation images\n",
            " |        'y_train': Array, shape (N_train,) of labels for training images\n",
            " |        'y_val': Array, shape (N_val,) of labels for validation images\n",
            " |      Optional arguments:\n",
            " |      - update_rule: A function of an update rule. Default is sgd.\n",
            " |      - optim_config: A dictionary containing hyperparameters that will be\n",
            " |        passed to the chosen update rule. Each update rule requires different\n",
            " |        hyperparameters but all update rules require a\n",
            " |        'learning_rate' parameter so that should always be present.\n",
            " |      - lr_decay: A scalar for learning rate decay; after each epoch the\n",
            " |        learning rate is multiplied by this value.\n",
            " |      - batch_size: Size of minibatches used to compute loss and gradient\n",
            " |        during training.\n",
            " |      - num_epochs: The number of epochs to run for during training.\n",
            " |      - print_every: Integer; training losses will be printed every\n",
            " |        print_every iterations.\n",
            " |      - print_acc_every: We will print the accuracy every print_acc_every epochs.\n",
            " |      - verbose: Boolean; if set to false then no output will be printed\n",
            " |        during training.\n",
            " |      - num_train_samples: Number of training samples used to check training\n",
            " |        accuracy; default is 1000; set to None to use entire training set.\n",
            " |      - num_val_samples: Number of validation samples to use to check val\n",
            " |        accuracy; default is None, which uses the entire validation set.\n",
            " |      - checkpoint_name: If not None, then save model checkpoints here every\n",
            " |        epoch.\n",
            " |  \n",
            " |  check_accuracy(self, X, y, num_samples=None, batch_size=100)\n",
            " |      Check accuracy of the model on the provided data.\n",
            " |      Inputs:\n",
            " |      - X: Array of data, of shape (N, d_1, ..., d_k)\n",
            " |      - y: Array of labels, of shape (N,)\n",
            " |      - num_samples: If not None, subsample the data and only test the model\n",
            " |        on num_samples datapoints.\n",
            " |      - batch_size: Split X and y into batches of this size to avoid using\n",
            " |        too much memory.\n",
            " |      Returns:\n",
            " |      - acc: Scalar giving the fraction of instances that were correctly\n",
            " |        classified by the model.\n",
            " |  \n",
            " |  train(self, time_limit=None)\n",
            " |      Run optimization to train the model.\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Static methods defined here:\n",
            " |  \n",
            " |  sgd(w, dw, config=None)\n",
            " |      Performs vanilla stochastic gradient descent.\n",
            " |      config format:\n",
            " |      - learning_rate: Scalar learning rate.\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Data descriptors defined here:\n",
            " |  \n",
            " |  __dict__\n",
            " |      dictionary for instance variables (if defined)\n",
            " |  \n",
            " |  __weakref__\n",
            " |      list of weak references to the object (if defined)\n",
            "\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6unJrOule1C_",
        "outputId": "c80a7e9e-4c77-4aed-cf89-9bdc32ae4259",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "fix_random_seed(0)\n",
        "model = TwoLayerNet(dtype=torch.float, device='cuda')\n",
        "solver = None\n",
        "\n",
        "##############################################################################\n",
        "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
        "# 50% accuracy on the validation set.                                        #\n",
        "##############################################################################\n",
        "# Replace \"pass\" statement with your code\n",
        "data = get_CIFAR10_data(visualize=False)\n",
        "solver = Solver(model, data,\n",
        "          optim_config={\n",
        "            'learning_rate': 3e-1,\n",
        "          },\n",
        "          lr_decay=0.95,\n",
        "          num_epochs=10, batch_size=200,\n",
        "          print_every=1000,\n",
        "          device='cuda')\n",
        "solver.train()\n",
        "solver.check_accuracy(data['X_train'], data['y_train'])\n",
        "##############################################################################\n",
        "#                             END OF YOUR CODE                               #\n",
        "##############################################################################"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(Time 0.01 sec; Iteration 1 / 2370) loss: 2.302585\n",
            "(Epoch 0 / 10) train acc: 0.103000; val_acc: 0.109200\n",
            "(Epoch 1 / 10) train acc: 0.388000; val_acc: 0.388400\n",
            "(Epoch 2 / 10) train acc: 0.510000; val_acc: 0.437600\n",
            "(Epoch 3 / 10) train acc: 0.533000; val_acc: 0.468400\n",
            "(Epoch 4 / 10) train acc: 0.536000; val_acc: 0.478400\n",
            "(Time 3.58 sec; Iteration 1001 / 2370) loss: 1.315438\n",
            "(Epoch 5 / 10) train acc: 0.534000; val_acc: 0.490000\n",
            "(Epoch 6 / 10) train acc: 0.532000; val_acc: 0.496400\n",
            "(Epoch 7 / 10) train acc: 0.580000; val_acc: 0.510000\n",
            "(Epoch 8 / 10) train acc: 0.581000; val_acc: 0.506000\n",
            "(Time 6.83 sec; Iteration 2001 / 2370) loss: 1.185759\n",
            "(Epoch 9 / 10) train acc: 0.569000; val_acc: 0.509600\n",
            "(Epoch 10 / 10) train acc: 0.587000; val_acc: 0.518000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5917263627052307"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gSSy7LTde1DE",
        "outputId": "fcb2c69a-394d-46df-dad0-27140a17d653",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        }
      },
      "source": [
        "# Run this cell to visualize training loss and train / val accuracy\n",
        "\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.title('Training loss')\n",
        "plt.plot(solver.loss_history, 'o')\n",
        "plt.xlabel('Iteration')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.title('Accuracy')\n",
        "plt.plot(solver.train_acc_history, '-o', label='train')\n",
        "plt.plot(solver.val_acc_history, '-o', label='val')\n",
        "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc='lower right')\n",
        "plt.gcf().set_size_inches(15, 12)\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eNyFLT1We1DI"
      },
      "source": [
        "# Multilayer network\n",
        "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
        "\n",
        "Read through the `FullyConnectedNet` class.\n",
        "\n",
        "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout; we will add this feature soon."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7p-goSyucyZH"
      },
      "source": [
        "class FullyConnectedNet(object):\n",
        "  \"\"\"\n",
        "  A fully-connected neural network with an arbitrary number of hidden layers,\n",
        "  ReLU nonlinearities, and a softmax loss function.\n",
        "  For a network with L layers, the architecture will be:\n",
        "\n",
        "  {linear - relu - [dropout]} x (L - 1) - linear - softmax\n",
        "\n",
        "  where dropout is optional, and the {...} block is repeated L - 1 times.\n",
        "\n",
        "  Similar to the TwoLayerNet above, learnable parameters are stored in the\n",
        "  self.params dictionary and will be learned using the Solver class.\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, hidden_dims, input_dim=3*32*32, num_classes=10,\n",
        "               dropout=0.0, reg=0.0, weight_scale=1e-2, seed=None,\n",
        "               dtype=torch.float, device='cpu'):\n",
        "    \"\"\"\n",
        "    Initialize a new FullyConnectedNet.\n",
        "\n",
        "    Inputs:\n",
        "    - hidden_dims: A list of integers giving the size of each hidden layer.\n",
        "    - input_dim: An integer giving the size of the input.\n",
        "    - num_classes: An integer giving the number of classes to classify.\n",
        "    - dropout: Scalar between 0 and 1 giving the drop probability for networks\n",
        "      with dropout. If dropout=0 then the network should not use dropout.\n",
        "    - reg: Scalar giving L2 regularization strength.\n",
        "    - weight_scale: Scalar giving the standard deviation for random\n",
        "      initialization of the weights.\n",
        "    - seed: If not None, then pass this random seed to the dropout layers. This\n",
        "      will make the dropout layers deteriminstic so we can gradient check the\n",
        "      model.\n",
        "    - dtype: A torch data type object; all computations will be performed using\n",
        "      this datatype. float is faster but less accurate, so you should use\n",
        "      double for numeric gradient checking.\n",
        "    - device: device to use for computation. 'cpu' or 'cuda'\n",
        "    \"\"\"\n",
        "    self.use_dropout = dropout != 0\n",
        "    self.reg = reg\n",
        "    self.num_layers = 1 + len(hidden_dims)\n",
        "    self.dtype = dtype\n",
        "    self.params = {}\n",
        "\n",
        "    ############################################################################\n",
        "    # TODO: Initialize the parameters of the network, storing all values in    #\n",
        "    # the self.params dictionary. Store weights and biases for the first layer #\n",
        "    # in W1 and b1; for the second layer use W2 and b2, etc. Weights should be #\n",
        "    # initialized from a normal distribution centered at 0 with standard       #\n",
        "    # deviation equal to weight_scale. Biases should be initialized to zero.   #\n",
        "    ############################################################################\n",
        "    # Replace \"pass\" statement with your code\n",
        "    kw = {'dtype': dtype, 'device': device}\n",
        "    self.params['W1'] = torch.randn((input_dim, hidden_dims[0]), **kw) * weight_scale\n",
        "    self.params['b1'] = torch.zeros(hidden_dims[0], **kw)\n",
        "\n",
        "    for i in range(1, self.num_layers-1):\n",
        "      self.params['W'+str(i+1)] = torch.randn((hidden_dims[i-1], hidden_dims[i]), **kw) * weight_scale\n",
        "      self.params['b'+str(i+1)] = torch.zeros(hidden_dims[i], **kw)\n",
        "\n",
        "    self.params['W'+str(self.num_layers)] = torch.randn((hidden_dims[-1], num_classes), **kw) * weight_scale\n",
        "    self.params['b'+str(self.num_layers)] = torch.zeros(num_classes, **kw)\n",
        "    ############################################################################\n",
        "    #                             END OF YOUR CODE                             #\n",
        "    ############################################################################\n",
        "\n",
        "    # When using dropout we need to pass a dropout_param dictionary to each\n",
        "    # dropout layer so that the layer knows the dropout probability and the mode\n",
        "    # (train / test). You can pass the same dropout_param to each dropout layer.\n",
        "    self.dropout_param = {}\n",
        "    if self.use_dropout > 0:\n",
        "      self.dropout_param = {'mode': 'train', 'p': dropout}\n",
        "      if seed is not None:\n",
        "        self.dropout_param['seed'] = seed\n",
        "\n",
        "\n",
        "  # def loss(self, X, y=None):\n",
        "  #   \"\"\"\n",
        "  #   Compute loss and gradient for the fully-connected net.\n",
        "  #   Input / output: Same as TwoLayerNet above.\n",
        "  #   \"\"\"\n",
        "  #   X = X.to(self.dtype)\n",
        "  #   mode = 'test' if y is None else 'train'\n",
        "\n",
        "  #   # Set train/test mode for batchnorm params and dropout param since they\n",
        "  #   # behave differently during training and testing.\n",
        "  #   if self.use_dropout:\n",
        "  #     self.dropout_param['mode'] = mode\n",
        "  #   scores = None\n",
        "  #   ############################################################################\n",
        "  #   # TODO: Implement the forward pass for the fully-connected net, computing  #\n",
        "  #   # the class scores for X and storing them in the scores variable.          #\n",
        "  #   #                                                                          #\n",
        "  #   # When using dropout, you'll need to pass self.dropout_param to each       #\n",
        "  #   # dropout forward pass.                                                    #\n",
        "  #   ############################################################################\n",
        "  #   # Replace \"pass\" statement with your code\n",
        "  #   cache = [None]* (self.num_layers)\n",
        "  #   dropout_cache = [None] * (self.num_layers)\n",
        "  #   hidden, cache[1] = Linear_ReLU.forward(\n",
        "  #       X, self.params['W1'], self.params['b1']\n",
        "  #   )\n",
        "  #   hidden, dropout_cache[1] = Dropout.forward(hidden, dropout_param)\n",
        "\n",
        "  #   for i in range(2, self.num_layers):\n",
        "  #     hidden, cache[i] = Linear_ReLU.forward(\n",
        "  #       hidden,  self.params['W'+str(i)], self.params['b'+str(i)]\n",
        "  #     )\n",
        "  #     hidden, dropout_cache[i] = Dropout.forward(hidden, dropout_param)\n",
        "\n",
        "  #   scores, cache[self.num_layers] = Linear.forward(\n",
        "  #       hidden, self.params['W'+str(self.num_layers)], self.params['b'+str(self.num_layers)]\n",
        "  #   )\n",
        "  #   ############################################################################\n",
        "  #   #                             END OF YOUR CODE                             #\n",
        "  #   ############################################################################\n",
        "\n",
        "  #   # If test mode return early\n",
        "  #   if mode == 'test':\n",
        "  #     return scores\n",
        "\n",
        "  #   loss, grads = 0.0, {}\n",
        "  #   ############################################################################\n",
        "  #   # TODO: Implement the backward pass for the fully-connected net. Store the #\n",
        "  #   # loss in the loss variable and gradients in the grads dictionary. Compute #\n",
        "  #   # data loss using softmax, and make sure that grads[k] holds the gradients #\n",
        "  #   # for self.params[k]. Don't forget to add L2 regularization!               #\n",
        "  #   # NOTE: To ensure that your implementation matches ours and you pass the   #\n",
        "  #   # automated tests, make sure that your L2 regularization includes a factor #\n",
        "  #   # of 0.5 to simplify the expression for the gradient.                      #\n",
        "  #   ############################################################################\n",
        "  #   # Replace \"pass\" statement with your code\n",
        "  #   loss, dout = softmax_loss(scores, y)\n",
        "  #   # res = (torch.tensor([self.params['W'+str(index)]**2 for index in range(1, self.num_layers + 1)])).sum()\n",
        "  #   res = sum([torch.sum(self.params['W'+str(index)] ** 2) for index in range(1, self.num_layers)])\n",
        "  #   loss += self.reg * res\n",
        "  #   d_h, grads['W'+str(self.num_layers)], grads['b'+str(self.num_layers)] = Linear.backward(dout, cache[-1])\n",
        "  #   d_h = Dropout.backward(d_h, dropout_cache[-1])\n",
        "\n",
        "  #   for i in range(self.num_layers-1, 1, -1):\n",
        "  #     d_h, grads['W'+str(i)], grads['b'+str(i)] = Linear_ReLU.backward(d_h, cache[i-1])\n",
        "  #     if i > 1:\n",
        "  #       d_h = Dropout.backward(d_h, dropout_cache[i-1])\n",
        "    \n",
        "  #   ############################################################################\n",
        "  #   #                             END OF YOUR CODE                             #\n",
        "  #   ############################################################################\n",
        "\n",
        "  #   return loss, grads\n",
        "  def loss(self, X, y=None):\n",
        "    X = X.to(self.dtype)\n",
        "    mode = 'test' if y is None else 'train'\n",
        "\n",
        "    if self.use_dropout:\n",
        "        self.dropout_param['mode'] = mode\n",
        "    scores = None\n",
        "    # The following code is copied from\n",
        "    # https://github.com/jasonbian97/Deep-Learning-Computer-Vision/blob/master/fully_connected_networks.ipynb\n",
        "    hidden_num = self.num_layers - 1\n",
        "    scores = X\n",
        "    cache_history = []\n",
        "    L2reg = 0\n",
        "\n",
        "    for i in range(hidden_num):\n",
        "        scores, cache = Linear_ReLU.forward(scores,\n",
        "                                            self.params['W%d' % (i + 1)],\n",
        "                                            self.params['b%d' % (i + 1)])\n",
        "        cache_history.append(cache)\n",
        "        if self.use_dropout:\n",
        "            scores, cache = Dropout.forward(scores, self.dropout_param)\n",
        "            cache_history.append(cache)\n",
        "        L2reg += torch.sum(self.params['W%d' % (i + 1)] ** 2)\n",
        "\n",
        "    i += 1\n",
        "    scores, cache = Linear.forward(scores, self.params['W%d' % (i + 1)],\n",
        "                                    self.params['b%d' % (i + 1)])\n",
        "    cache_history.append(cache)\n",
        "    L2reg += torch.sum(self.params['W%d' % (i + 1)] ** 2)\n",
        "    L2reg *= self.reg\n",
        "\n",
        "    if mode == 'test':\n",
        "        return scores\n",
        "\n",
        "    loss, grads = 0.0, {}\n",
        "    loss, dout = softmax_loss(scores, y)\n",
        "    dout, grads['W%d' % (i + 1)], grads['b%d' % (i + 1)] = Linear.backward(dout, cache_history.pop())\n",
        "    grads['W%d' % (i + 1)] += 2.0 * self.reg * self.params['W%d' % (i + 1)]\n",
        "    i -= 1\n",
        "    \n",
        "    while i >= 0:\n",
        "        if self.use_dropout:\n",
        "            dout = Dropout.backward(dout, cache_history.pop())\n",
        "        dout, grads['W%d' % (i + 1)], grads['b%d' % (i + 1)] = Linear_ReLU.backward(dout, cache_history.pop())\n",
        "        grads['W%d' % (i + 1)] += self.reg * 2.0 * self.params['W%d' % (i + 1)]\n",
        "        i -= 1\n",
        "    return loss, grads"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3abR1_qhe1DK"
      },
      "source": [
        "## Initial loss and gradient check\n",
        "\n",
        "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
        "\n",
        "For gradient checking, you should expect to see errors less than `1e-6`, except for the check on `W1` and `W2` with `reg=0` where your errors should be less than `1e-5`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1waPtKRDe1DL",
        "outputId": "6cabb6d7-5bf5-4773-bf1f-27c4c7cbedc7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "fix_random_seed(0)\n",
        "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
        "X = torch.randn(N, D, **to_double_cuda) # 2 x 15\n",
        "y = torch.randint(C, size=(N,), **to_long_cuda) # 2,\n",
        "\n",
        "for reg in [0, 3.14]:\n",
        "  print('Running check with reg = ', reg)\n",
        "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
        "                            reg=reg, weight_scale=5e-2, **to_double_cuda)\n",
        "\n",
        "  loss, grads = model.loss(X, y)\n",
        "  print('Initial loss: ', loss.item())\n",
        "\n",
        "  for name in sorted(grads):\n",
        "    f = lambda _: model.loss(X, y)[0]\n",
        "    grad_num = compute_numeric_gradient(f, model.params[name])\n",
        "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running check with reg =  0\n",
            "Initial loss:  2.3053575717037686\n",
            "W1 relative error: 7.62e-06\n",
            "W2 relative error: 3.47e-07\n",
            "W3 relative error: 1.39e-07\n",
            "b1 relative error: 4.20e-07\n",
            "b2 relative error: 3.76e-09\n",
            "b3 relative error: 2.34e-10\n",
            "Running check with reg =  3.14\n",
            "Initial loss:  2.309034632887848\n",
            "W1 relative error: 3.29e+00\n",
            "W2 relative error: 1.68e+00\n",
            "W3 relative error: 1.80e+00\n",
            "b1 relative error: 4.81e-08\n",
            "b2 relative error: 1.34e-08\n",
            "b3 relative error: 2.22e-10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-q6aWzNfe1DQ"
      },
      "source": [
        "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. In the following cell, tweak the **learning rate** and **weight initialization scale** to overfit and achieve 100% training accuracy within 20 epochs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2NccCDJ3e1DR",
        "outputId": "4065d1eb-39f4-4c97-90a5-a5e1cbf6f8d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 958
        }
      },
      "source": [
        "# TODO: Use a three-layer Net to overfit 50 training examples by \n",
        "# tweaking just the learning rate and initialization scale.\n",
        "fix_random_seed(0)\n",
        "\n",
        "num_train = 50\n",
        "small_data = {\n",
        "  'X_train': data_dict['X_train'][:num_train],\n",
        "  'y_train': data_dict['y_train'][:num_train],\n",
        "  'X_val': data_dict['X_val'],\n",
        "  'y_val': data_dict['y_val'],\n",
        "}\n",
        "\n",
        "weight_scale = 1e-1   # Experiment with this!\n",
        "learning_rate = 1e-1  # Experiment with this!\n",
        "############################################################################\n",
        "# TODO: Change weight_scale and learning_rate so your model achieves 100%  #\n",
        "# training accuracy within 20 epochs.                                      #\n",
        "############################################################################\n",
        "# Replace \"pass\" statement with your code\n",
        "weight_scale = 2e-1   # Experiment with this!\n",
        "learning_rate = 1e-1  # Experiment with this!\n",
        "\n",
        "############################################################################\n",
        "#                             END OF YOUR CODE                             #\n",
        "############################################################################\n",
        "model = FullyConnectedNet([100, 100],\n",
        "              weight_scale=weight_scale, **to_float_cuda)\n",
        "solver = Solver(model, small_data,\n",
        "                print_every=10, num_epochs=20, batch_size=25,\n",
        "                optim_config={\n",
        "                  'learning_rate': learning_rate,\n",
        "                },\n",
        "                device='cuda',\n",
        "         )\n",
        "solver.train()\n",
        "\n",
        "plt.plot(solver.loss_history, 'o')\n",
        "plt.title('Training loss history')\n",
        "plt.xlabel('Iteration')\n",
        "plt.ylabel('Training loss')\n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(Time 0.00 sec; Iteration 1 / 40) loss: 8.241880\n",
            "(Epoch 0 / 20) train acc: 0.200000; val_acc: 0.142800\n",
            "(Epoch 1 / 20) train acc: 0.400000; val_acc: 0.114800\n",
            "(Epoch 2 / 20) train acc: 0.440000; val_acc: 0.151600\n",
            "(Epoch 3 / 20) train acc: 0.800000; val_acc: 0.144400\n",
            "(Epoch 4 / 20) train acc: 0.940000; val_acc: 0.137200\n",
            "(Epoch 5 / 20) train acc: 0.980000; val_acc: 0.147200\n",
            "(Time 0.14 sec; Iteration 11 / 40) loss: 0.112686\n",
            "(Epoch 6 / 20) train acc: 1.000000; val_acc: 0.148400\n",
            "(Epoch 7 / 20) train acc: 1.000000; val_acc: 0.149200\n",
            "(Epoch 8 / 20) train acc: 1.000000; val_acc: 0.150800\n",
            "(Epoch 9 / 20) train acc: 1.000000; val_acc: 0.148800\n",
            "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.149600\n",
            "(Time 0.25 sec; Iteration 21 / 40) loss: 0.022414\n",
            "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.148000\n",
            "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.148400\n",
            "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.147600\n",
            "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.148000\n",
            "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.147600\n",
            "(Time 0.37 sec; Iteration 31 / 40) loss: 0.014234\n",
            "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.147200\n",
            "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.147200\n",
            "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.148400\n",
            "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.148000\n",
            "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.148400\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tskjw8VUe1DV"
      },
      "source": [
        "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again, you will have to adjust the learning rate and weight initialization scale, but you should be able to achieve 100% training accuracy within 20 epochs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D5mAWrrPe1Dc",
        "outputId": "5f2d8285-3a3e-4c86-e800-a98657d2dcea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 958
        }
      },
      "source": [
        "# TODO: Use a five-layer Net to overfit 50 training examples by \n",
        "# tweaking just the learning rate and initialization scale.\n",
        "fix_random_seed(0)\n",
        "\n",
        "num_train = 50\n",
        "small_data = {\n",
        "  'X_train': data_dict['X_train'][:num_train],\n",
        "  'y_train': data_dict['y_train'][:num_train],\n",
        "  'X_val': data_dict['X_val'],\n",
        "  'y_val': data_dict['y_val'],\n",
        "}\n",
        "\n",
        "learning_rate = 3e-1  # Experiment with this!\n",
        "weight_scale = 1e-1   # Experiment with this!\n",
        "############################################################################\n",
        "# TODO: Change weight_scale and learning_rate so your model achieves 100%  #\n",
        "# training accuracy within 20 epochs.                                      #\n",
        "############################################################################\n",
        "# Replace \"pass\" statement with your code\n",
        "learning_rate = 3e-1  # Experiment with this!\n",
        "weight_scale = 1e-1   # Experiment with this!\n",
        "############################################################################\n",
        "#                             END OF YOUR CODE                             #\n",
        "############################################################################\n",
        "model = FullyConnectedNet([100, 100, 100, 100],\n",
        "                weight_scale=weight_scale, **to_float_cuda)\n",
        "solver = Solver(model, small_data,\n",
        "                print_every=10, num_epochs=20, batch_size=25,\n",
        "                optim_config={\n",
        "                  'learning_rate': learning_rate,\n",
        "                },\n",
        "                device='cuda',\n",
        "         )\n",
        "solver.train()\n",
        "\n",
        "plt.plot(solver.loss_history, 'o')\n",
        "plt.title('Training loss history')\n",
        "plt.xlabel('Iteration')\n",
        "plt.ylabel('Training loss')\n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(Time 0.01 sec; Iteration 1 / 40) loss: 2.262105\n",
            "(Epoch 0 / 20) train acc: 0.160000; val_acc: 0.108000\n",
            "(Epoch 1 / 20) train acc: 0.440000; val_acc: 0.129200\n",
            "(Epoch 2 / 20) train acc: 0.480000; val_acc: 0.132400\n",
            "(Epoch 3 / 20) train acc: 0.380000; val_acc: 0.124000\n",
            "(Epoch 4 / 20) train acc: 0.200000; val_acc: 0.108800\n",
            "(Epoch 5 / 20) train acc: 0.540000; val_acc: 0.155600\n",
            "(Time 0.19 sec; Iteration 11 / 40) loss: 1.542213\n",
            "(Epoch 6 / 20) train acc: 0.780000; val_acc: 0.181600\n",
            "(Epoch 7 / 20) train acc: 0.760000; val_acc: 0.173200\n",
            "(Epoch 8 / 20) train acc: 0.460000; val_acc: 0.124800\n",
            "(Epoch 9 / 20) train acc: 0.840000; val_acc: 0.177200\n",
            "(Epoch 10 / 20) train acc: 0.920000; val_acc: 0.174000\n",
            "(Time 0.35 sec; Iteration 21 / 40) loss: 0.548559\n",
            "(Epoch 11 / 20) train acc: 0.960000; val_acc: 0.185600\n",
            "(Epoch 12 / 20) train acc: 0.960000; val_acc: 0.183200\n",
            "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.187600\n",
            "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.186800\n",
            "(Epoch 15 / 20) train acc: 0.940000; val_acc: 0.186800\n",
            "(Time 0.51 sec; Iteration 31 / 40) loss: 0.129616\n",
            "(Epoch 16 / 20) train acc: 0.980000; val_acc: 0.186800\n",
            "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.189200\n",
            "(Epoch 18 / 20) train acc: 0.980000; val_acc: 0.196400\n",
            "(Epoch 19 / 20) train acc: 0.980000; val_acc: 0.184800\n",
            "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.192000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4eWrnY7e1Di"
      },
      "source": [
        "# Update rules\n",
        "So far we have used **vanilla stochastic gradient descent (SGD)** as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBDJqbeVe1Dn"
      },
      "source": [
        "## SGD+Momentum\n",
        "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochastic gradient descent. See the Momentum Update section at http://cs231n.github.io/neural-networks-3/#sgd for more information.\n",
        "\n",
        "We will implement various first-order update rules that are commonly used\n",
        "for training neural networks. Each update rule accepts current weights and the\n",
        "gradient of the loss with respect to those weights and produces the next set of\n",
        "weights. Each update rule has the same interface:\n",
        "```python\n",
        "def update(w, dw, config=None):\n",
        "Inputs:\n",
        "  - w: A tensor giving the current weights.\n",
        "  - dw: A tensor of the same shape as w giving the gradient of the\n",
        "    loss with respect to w.\n",
        "  - config: A dictionary containing hyperparameter values such as learning\n",
        "    rate, momentum, etc. If the update rule requires caching values over many\n",
        "    iterations, then config will also hold these cached values.\n",
        "Returns:\n",
        "  - next_w: The next point after the update.\n",
        "  - config: The config dictionary to be passed to the next iteration of the\n",
        "    update rule.\n",
        "NOTE: For most update rules, the default learning rate will probably not\n",
        "perform well; however the default values of the other hyperparameters should\n",
        "work well for a variety of different problems.\n",
        "For efficiency, update rules may perform in-place updates, mutating w and\n",
        "setting next_w equal to w.\n",
        "```\n",
        "We provide the implementation of the SGD update rule for your reference:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NT42gX8IBU6s"
      },
      "source": [
        "def sgd(w, dw, config=None):\n",
        "    \"\"\"\n",
        "    Performs vanilla stochastic gradient descent.\n",
        "    config format:\n",
        "    - learning_rate: Scalar learning rate.\n",
        "    \"\"\"\n",
        "    if config is None: config = {}\n",
        "    config.setdefault('learning_rate', 1e-2)\n",
        "\n",
        "    w -= config['learning_rate'] * dw\n",
        "    return w, config"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVbUveEWBXoI"
      },
      "source": [
        "Now implement the SGD+Momentum update rule using the same interface:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ym9RjlaRXakL"
      },
      "source": [
        "def sgd_momentum(w, dw, config=None):\n",
        "  \"\"\"\n",
        "  Performs stochastic gradient descent with momentum.\n",
        "  config format:\n",
        "  - learning_rate: Scalar learning rate.\n",
        "  - momentum: Scalar between 0 and 1 giving the momentum value.\n",
        "    Setting momentum = 0 reduces to sgd.\n",
        "  - velocity: A numpy array of the same shape as w and dw used to store a\n",
        "    moving average of the gradients.\n",
        "  \"\"\"\n",
        "  if config is None: config = {}\n",
        "  config.setdefault('learning_rate', 1e-2)\n",
        "  config.setdefault('momentum', 0.9)\n",
        "  v = config.get('velocity', torch.zeros_like(w))\n",
        "\n",
        "  next_w = None\n",
        "  #############################################################################\n",
        "  # TODO: Implement the momentum update formula. Store the updated value in   #\n",
        "  # the next_w variable. You should also use and update the velocity v.       #\n",
        "  #############################################################################\n",
        "  # Replace \"pass\" statement with your code\n",
        "  v = config['momentum'] * v - config['learning_rate'] * dw\n",
        "  next_w = w + v\n",
        "  #############################################################################\n",
        "  #                              END OF YOUR CODE                             #\n",
        "  #############################################################################\n",
        "  config['velocity'] = v\n",
        "\n",
        "  return next_w, config"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9KREnzX_Bgl4"
      },
      "source": [
        "Run the following to check your implementation of SGD+Momentum. You should see errors less than `1e-7`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbQrkNo_e1Dp",
        "outputId": "8c4bec27-02a7-4c8e-f23f-7b59ad411852",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "fix_random_seed(0)\n",
        "\n",
        "N, D = 4, 5\n",
        "w = torch.linspace(-0.4, 0.6, steps=N*D, **to_double_cuda).reshape(N, D)\n",
        "dw = torch.linspace(-0.6, 0.4, steps=N*D, **to_double_cuda).reshape(N, D)\n",
        "v = torch.linspace(0.6, 0.9, steps=N*D, **to_double_cuda).reshape(N, D)\n",
        "\n",
        "config = {'learning_rate': 1e-3, 'velocity': v}\n",
        "next_w, _ = sgd_momentum(w, dw, config=config)\n",
        "\n",
        "expected_next_w = torch.tensor([\n",
        "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
        "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
        "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
        "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]],\n",
        "   **to_double_cuda)\n",
        "expected_velocity = torch.tensor([\n",
        "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
        "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
        "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
        "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]],\n",
        "   **to_double_cuda)\n",
        "\n",
        "# Should see relative errors around e-8 or less\n",
        "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
        "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "next_w error:  1.7764694224803817e-08\n",
            "velocity error:  8.538575450103691e-09\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7QQj73zje1D2"
      },
      "source": [
        "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXdMNC9Ve1D4",
        "outputId": "ae2e0683-edf2-4276-f74f-e020f2a04a47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "num_train = 4000\n",
        "small_data = {\n",
        "  'X_train': data_dict['X_train'][:num_train],\n",
        "  'y_train': data_dict['y_train'][:num_train],\n",
        "  'X_val': data_dict['X_val'],\n",
        "  'y_val': data_dict['y_val'],\n",
        "}\n",
        "\n",
        "solvers = {}\n",
        "\n",
        "for update_rule_name, update_rule_fn in [('sgd', sgd), ('sgd_momentum', sgd_momentum)]:\n",
        "  print('running with ', update_rule_name)\n",
        "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2,\n",
        "                            **to_float_cuda)\n",
        "\n",
        "  solver = Solver(model, small_data,\n",
        "                  num_epochs=5, batch_size=100,\n",
        "                  update_rule=update_rule_fn,\n",
        "                  optim_config={\n",
        "                    'learning_rate': 5e-2,\n",
        "                  },\n",
        "                  print_every=1000,\n",
        "                  verbose=True,\n",
        "                  device='cuda')\n",
        "  solvers[update_rule_name] = solver\n",
        "  solver.train()\n",
        "  print()\n",
        "  \n",
        "plt.subplot(3, 1, 1)\n",
        "plt.title('Training loss')\n",
        "plt.xlabel('Iteration')\n",
        "for update_rule, solver in solvers.items():\n",
        "  plt.plot(solver.loss_history, 'o', label=\"loss_%s\" % update_rule)\n",
        "plt.legend(loc='upper center', ncol=4)\n",
        "  \n",
        "plt.subplot(3, 1, 2)\n",
        "plt.title('Training accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "for update_rule, solver in solvers.items():\n",
        "  plt.plot(solver.train_acc_history, '-o', label=\"train_acc_%s\" % update_rule)\n",
        "plt.legend(loc='upper center', ncol=4)\n",
        "\n",
        "  \n",
        "plt.subplot(3, 1, 3)\n",
        "plt.title('Validation accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "for update_rule, solver in solvers.items():\n",
        "  plt.plot(solver.val_acc_history, '-o', label=\"val_acc_%s\" % update_rule)\n",
        "plt.legend(loc='upper center', ncol=4)\n",
        "\n",
        "plt.gcf().set_size_inches(15, 15)\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "running with  sgd\n",
            "(Time 0.01 sec; Iteration 1 / 200) loss: 2.302603\n",
            "(Epoch 0 / 5) train acc: 0.116000; val_acc: 0.090000\n",
            "(Epoch 1 / 5) train acc: 0.098000; val_acc: 0.093600\n",
            "(Epoch 2 / 5) train acc: 0.094000; val_acc: 0.093600\n",
            "(Epoch 3 / 5) train acc: 0.117000; val_acc: 0.093600\n",
            "(Epoch 4 / 5) train acc: 0.105000; val_acc: 0.090000\n",
            "(Epoch 5 / 5) train acc: 0.092000; val_acc: 0.093600\n",
            "\n",
            "running with  sgd_momentum\n",
            "(Time 0.00 sec; Iteration 1 / 200) loss: 2.302174\n",
            "(Epoch 0 / 5) train acc: 0.108000; val_acc: 0.100800\n",
            "(Epoch 1 / 5) train acc: 0.099000; val_acc: 0.087600\n",
            "(Epoch 2 / 5) train acc: 0.167000; val_acc: 0.184400\n",
            "(Epoch 3 / 5) train acc: 0.164000; val_acc: 0.162000\n",
            "(Epoch 4 / 5) train acc: 0.285000; val_acc: 0.254800\n",
            "(Epoch 5 / 5) train acc: 0.262000; val_acc: 0.248000\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYtKqDdEe1D-"
      },
      "source": [
        "## RMSProp\n",
        "RMSProp [1] is an update rule that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
        "\n",
        "Implement the RMSProp update rule in the `rmsprop` function below:\n",
        "\n",
        "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zy_OhbVEYVqz"
      },
      "source": [
        "def rmsprop(w, dw, config=None):\n",
        "  \"\"\"\n",
        "  Uses the RMSProp update rule, which uses a moving average of squared\n",
        "  gradient values to set adaptive per-parameter learning rates.\n",
        "  config format:\n",
        "  - learning_rate: Scalar learning rate.\n",
        "  - decay_rate: Scalar between 0 and 1 giving the decay rate for the squared\n",
        "    gradient cache.\n",
        "  - epsilon: Small scalar used for smoothing to avoid dividing by zero.\n",
        "  - cache: Moving average of second moments of gradients.\n",
        "  \"\"\"\n",
        "  if config is None: config = {}\n",
        "  config.setdefault('learning_rate', 1e-2)\n",
        "  config.setdefault('decay_rate', 0.99)\n",
        "  config.setdefault('epsilon', 1e-8)\n",
        "  config.setdefault('cache', torch.zeros_like(w))\n",
        "\n",
        "  next_w = None\n",
        "  ###########################################################################\n",
        "  # TODO: Implement the RMSprop update formula, storing the next value of w #\n",
        "  # in the next_w variable. Don't forget to update cache value stored in    #\n",
        "  # config['cache'].                                                        #\n",
        "  ###########################################################################\n",
        "  # Replace \"pass\" statement with your code\n",
        "  config['cache'] = config['decay_rate'] * config['cache'] + (1-config['decay_rate'])*(dw ** 2)\n",
        "  next_w = w - config['learning_rate'] * dw / torch.sqrt(config['cache'] + config['epsilon'])\n",
        "  ###########################################################################\n",
        "  #                             END OF YOUR CODE                            #\n",
        "  ###########################################################################\n",
        "\n",
        "  return next_w, config"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izr7eqIxDrZs"
      },
      "source": [
        "Run the following to test your RMSProp implementation. You should see errors less than `1e-6`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBBpJhJie1D_",
        "outputId": "0d402024-4fb9-41e4-e8df-13fea7aac304",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Test RMSProp implementation\n",
        "fix_random_seed(0)\n",
        "\n",
        "N, D = 4, 5\n",
        "w = torch.linspace(-0.4, 0.6, steps=N*D, **to_double_cuda).reshape(N, D)\n",
        "dw = torch.linspace(-0.6, 0.4, steps=N*D, **to_double_cuda).reshape(N, D)\n",
        "cache = torch.linspace(0.6, 0.9, steps=N*D, **to_double_cuda).reshape(N, D)\n",
        "\n",
        "config = {'learning_rate': 1e-2, 'cache': cache}\n",
        "next_w, _ = rmsprop(w, dw, config=config)\n",
        "\n",
        "expected_next_w = torch.tensor([\n",
        "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
        "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
        "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
        "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]],\n",
        "   **to_double_cuda)\n",
        "expected_cache = torch.tensor([\n",
        "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
        "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
        "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
        "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]],\n",
        "   **to_double_cuda)\n",
        "\n",
        "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
        "print('cache error: ', rel_error(expected_cache, config['cache']))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "next_w error:  1.9005292265794088e-07\n",
            "cache error:  5.2955911474095824e-09\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bMdq7WRFDiJw"
      },
      "source": [
        "## Adam\n",
        "Adam [2] extends RMSprop with a first-order gradient cache similar to momentum, and a bias correction mechanism to prevent large steps at the start of optimization. Adam is one of the most commonly used update rules used in practice for training deep neural networks.\n",
        "\n",
        "Implement the Adam update rule in the `adam` function below:\n",
        "\n",
        "**NOTE:** Please implement the _complete_ Adam update rule (with the bias correction mechanism), not the first simplified version mentioned in the course notes. \n",
        "\n",
        "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GBXOxGn-DK1E"
      },
      "source": [
        "def adam(w, dw, config=None):\n",
        "  \"\"\"\n",
        "  Uses the Adam update rule, which incorporates moving averages of both the\n",
        "  gradient and its square and a bias correction term.\n",
        "  config format:\n",
        "  - learning_rate: Scalar learning rate.\n",
        "  - beta1: Decay rate for moving average of first moment of gradient.\n",
        "  - beta2: Decay rate for moving average of second moment of gradient.\n",
        "  - epsilon: Small scalar used for smoothing to avoid dividing by zero.\n",
        "  - m: Moving average of gradient.\n",
        "  - v: Moving average of squared gradient.\n",
        "  - t: Iteration number.\n",
        "  \"\"\"\n",
        "  if config is None: config = {}\n",
        "  config.setdefault('learning_rate', 1e-3)\n",
        "  config.setdefault('beta1', 0.9)\n",
        "  config.setdefault('beta2', 0.999)\n",
        "  config.setdefault('epsilon', 1e-8)\n",
        "  config.setdefault('m', torch.zeros_like(w))\n",
        "  config.setdefault('v', torch.zeros_like(w))\n",
        "  config.setdefault('t', 0)\n",
        "\n",
        "  next_w = None\n",
        "  #############################################################################\n",
        "  # TODO: Implement the Adam update formula, storing the next value of w in   #\n",
        "  # the next_w variable. Don't forget to update the m, v, and t variables     #\n",
        "  # stored in config.                                                         #\n",
        "  #                                                                           #\n",
        "  # NOTE: In order to match the reference output, please modify t _before_    #\n",
        "  # using it in any calculations.                                             #\n",
        "  #############################################################################\n",
        "  config['t'] += 1\n",
        "  config['m'] = config['beta1'] * config['m'] + (1.0 - config['beta1']) * dw\n",
        "  config['v'] = config['beta2'] * config['v'] + (1.0 - config['beta2']) * (dw**2)\n",
        "  moment1 = config['m'] / (1 - config['beta1']**config['t'])\n",
        "  moment2 = config['v'] / (1 - config['beta2']**config['t'])\n",
        "  next_w = w - config['learning_rate'] * moment1 / (torch.sqrt(moment2) + config['epsilon'])\n",
        "  #############################################################################\n",
        "  #                              END OF YOUR CODE                             #\n",
        "  #############################################################################\n",
        "\n",
        "  return next_w, config"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIEwAIYCEOnO"
      },
      "source": [
        "Run the following to test your Adam implementation. You should see error less than `1e-6` for `next_w`, and errors less than `1e-8` for `v` and `m`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ovUXV51Le1EE",
        "outputId": "16e37d61-1958-44ba-dab5-7352ec323fab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Test Adam implementation\n",
        "fix_random_seed(0)\n",
        "\n",
        "N, D = 4, 5\n",
        "w = torch.linspace(-0.4, 0.6, steps=N*D, **to_double_cuda).reshape(N, D)\n",
        "dw = torch.linspace(-0.6, 0.4, steps=N*D, **to_double_cuda).reshape(N, D)\n",
        "m = torch.linspace(0.6, 0.9, steps=N*D, **to_double_cuda).reshape(N, D)\n",
        "v = torch.linspace(0.7, 0.5, steps=N*D, **to_double_cuda).reshape(N, D)\n",
        "\n",
        "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
        "next_w, _ = adam(w, dw, config=config)\n",
        "\n",
        "expected_next_w = torch.tensor([\n",
        "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
        "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
        "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
        "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]],\n",
        "   **to_double_cuda)\n",
        "expected_v = torch.tensor([\n",
        "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
        "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
        "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
        "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]],\n",
        "   **to_double_cuda)\n",
        "expected_m = torch.tensor([\n",
        "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
        "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
        "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
        "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]],\n",
        "   **to_double_cuda)\n",
        "\n",
        "# You should see relative errors around e-7 or less\n",
        "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
        "print('v error: ', rel_error(expected_v, config['v']))\n",
        "print('m error: ', rel_error(expected_m, config['m']))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "next_w error:  2.279138619430699e-07\n",
            "v error:  8.416628040806327e-09\n",
            "m error:  8.429926350697003e-09\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1T_qzgxte1EI"
      },
      "source": [
        "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TFopQgre1EJ",
        "outputId": "3f77c446-70b8-465c-aabb-ff2d715d8430",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for update_rule_name, update_rule_fn, learning_rate in [('adam', adam, 1e-3), ('rmsprop', rmsprop, 1e-4)]:\n",
        "  print('running with ', update_rule)\n",
        "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2, device='cuda')\n",
        "\n",
        "  solver = Solver(model, small_data,\n",
        "                  num_epochs=5, batch_size=100,\n",
        "                  update_rule=update_rule_fn,\n",
        "                  optim_config={\n",
        "                    'learning_rate': learning_rate\n",
        "                  },\n",
        "                  print_every=1000,\n",
        "                  verbose=True, device='cuda')\n",
        "  solvers[update_rule_name] = solver\n",
        "  solver.train()\n",
        "  print()\n",
        "\n",
        "plt.subplot(3, 1, 1)\n",
        "plt.title('Training loss')\n",
        "plt.xlabel('Iteration')\n",
        "for update_rule, solver in list(solvers.items()):\n",
        "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
        "plt.legend(loc='lower center', ncol=4)\n",
        "  \n",
        "plt.subplot(3, 1, 2)\n",
        "plt.title('Training accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "for update_rule, solver in list(solvers.items()):\n",
        "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
        "plt.legend(loc='upper center', ncol=4)\n",
        "\n",
        "plt.subplot(3, 1, 3)\n",
        "plt.title('Validation accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "for update_rule, solver in list(solvers.items()):\n",
        "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
        "plt.legend(loc='upper center', ncol=4)\n",
        "\n",
        "plt.gcf().set_size_inches(15, 15)\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "running with  sgd_momentum\n",
            "(Time 0.01 sec; Iteration 1 / 200) loss: 2.302603\n",
            "(Epoch 0 / 5) train acc: 0.110000; val_acc: 0.105200\n",
            "(Epoch 1 / 5) train acc: 0.279000; val_acc: 0.267600\n",
            "(Epoch 2 / 5) train acc: 0.340000; val_acc: 0.282800\n",
            "(Epoch 3 / 5) train acc: 0.383000; val_acc: 0.322800\n",
            "(Epoch 4 / 5) train acc: 0.400000; val_acc: 0.352000\n",
            "(Epoch 5 / 5) train acc: 0.464000; val_acc: 0.370800\n",
            "\n",
            "running with  sgd_momentum\n",
            "(Time 0.00 sec; Iteration 1 / 200) loss: 2.302174\n",
            "(Epoch 0 / 5) train acc: 0.108000; val_acc: 0.110000\n",
            "(Epoch 1 / 5) train acc: 0.171000; val_acc: 0.168000\n",
            "(Epoch 2 / 5) train acc: 0.184000; val_acc: 0.172400\n",
            "(Epoch 3 / 5) train acc: 0.223000; val_acc: 0.213600\n",
            "(Epoch 4 / 5) train acc: 0.258000; val_acc: 0.246400\n",
            "(Epoch 5 / 5) train acc: 0.245000; val_acc: 0.245200\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2_BL-2TwxKR",
        "tags": [
          "pdf-title"
        ]
      },
      "source": [
        "# Dropout\n",
        "Dropout [1] is a technique for regularizing neural networks by randomly setting some output activations to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to optionally use dropout.\n",
        "\n",
        "[1] [Geoffrey E. Hinton et al, \"Improving neural networks by preventing co-adaptation of feature detectors\", arXiv 2012](https://arxiv.org/abs/1207.0580)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NlSCDbeLZEhq"
      },
      "source": [
        "class Dropout(object):\n",
        "\n",
        "  @staticmethod\n",
        "  def forward(x, w, b):\n",
        "    raise NotImplementedError\n",
        "\n",
        "  @staticmethod\n",
        "  def backward(dout, cache):\n",
        "    raise NotImplementedError"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s68cb0QBwxKj"
      },
      "source": [
        "## Dropout: forward\n",
        "Implement the forward pass for dropout. Since dropout behaves differently during training and testing, make sure to implement the operation for both modes.\n",
        "\n",
        "Once you have done so, run the cell below to test your implementation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vt_V_ICfY38-"
      },
      "source": [
        "def dropout_forward(x, dropout_param):\n",
        "  \"\"\"\n",
        "  Performs the forward pass for (inverted) dropout.\n",
        "  Inputs:\n",
        "  - x: Input data: tensor of any shape\n",
        "  - dropout_param: A dictionary with the following keys:\n",
        "    - p: Dropout parameter. We *drop* each neuron output with probability p.\n",
        "    - mode: 'test' or 'train'. If the mode is train, then perform dropout;\n",
        "    if the mode is test, then just return the input.\n",
        "    - seed: Seed for the random number generator. Passing seed makes this\n",
        "    function deterministic, which is needed for gradient checking but not\n",
        "    in real networks.\n",
        "  Outputs:\n",
        "  - out: Tensor of the same shape as x.\n",
        "  - cache: tuple (dropout_param, mask). In training mode, mask is the dropout\n",
        "    mask that was used to multiply the input; in test mode, mask is None.\n",
        "  NOTE: Please implement **inverted** dropout, not the vanilla version of dropout.\n",
        "  See http://cs231n.github.io/neural-networks-2/#reg for more details.\n",
        "  NOTE 2: Keep in mind that p is the probability of **dropping** a neuron\n",
        "  output; this might be contrary to some sources, where it is referred to\n",
        "  as the probability of keeping a neuron output.\n",
        "  \"\"\"\n",
        "  p, mode = dropout_param['p'], dropout_param['mode']\n",
        "  if 'seed' in dropout_param:\n",
        "    torch.manual_seed(dropout_param['seed'])\n",
        "\n",
        "  mask = None\n",
        "  out = None\n",
        "\n",
        "  if mode == 'train':\n",
        "    ###########################################################################\n",
        "    # TODO: Implement training phase forward pass for inverted dropout.       #\n",
        "    # Store the dropout mask in the mask variable.                            #\n",
        "    ###########################################################################\n",
        "    # Replace \"pass\" statement with your code\n",
        "    mask = ((torch.randn(*x.shape, dtype=x.dtype, device=x.device) < p) / p)\n",
        "    x *= mask\n",
        "    out = x\n",
        "    ###########################################################################\n",
        "    #                             END OF YOUR CODE                            #\n",
        "    ###########################################################################\n",
        "  elif mode == 'test':\n",
        "    ###########################################################################\n",
        "    # TODO: Implement the test phase forward pass for inverted dropout.       #\n",
        "    ###########################################################################\n",
        "    # Replace \"pass\" statement with your code\n",
        "    out = x\n",
        "    ###########################################################################\n",
        "    #                             END OF YOUR CODE                            #\n",
        "    ###########################################################################\n",
        "\n",
        "  cache = (dropout_param, mask)\n",
        "\n",
        "  return out, cache\n",
        "\n",
        "Dropout.forward = dropout_forward"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFns3S76Gxy_"
      },
      "source": [
        "Run the following to test your dropout implementation. The mean of the output should be approximately the same during training and testing. During training the number of outputs set to zero should be approximately equal to the drop probability `p`, and during testing no outputs should be set to zero."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vFAmI9VxwxKk",
        "outputId": "f5f6be67-2757-4772-f01c-f11b2348676d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "fix_random_seed(0)\n",
        "x = torch.randn(500, 500, **to_double_cuda) + 10\n",
        "\n",
        "for p in [0.25, 0.4, 0.7]:\n",
        "  out, _ = Dropout.forward(x, {'mode': 'train', 'p': p})\n",
        "  out_test, _ = Dropout.forward(x, {'mode': 'test', 'p': p})\n",
        "\n",
        "  print('Running tests with p = ', p)\n",
        "  print('Mean of input: ', x.mean().item())\n",
        "  print('Mean of train-time output: ', out.mean().item())\n",
        "  print('Mean of test-time output: ', out_test.mean().item())\n",
        "  print('Fraction of train-time output set to zero: ', (out == 0).type(torch.float32).mean().item())\n",
        "  print('Fraction of test-time output set to zero: ', (out_test == 0).type(torch.float32).mean().item())\n",
        "  print()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running tests with p =  0.25\n",
            "Mean of input:  23.951260386763323\n",
            "Mean of train-time output:  23.951260386763323\n",
            "Mean of test-time output:  23.951260386763323\n",
            "Fraction of train-time output set to zero:  0.40106400847435\n",
            "Fraction of test-time output set to zero:  0.40106400847435\n",
            "\n",
            "Running tests with p =  0.4\n",
            "Mean of input:  39.31542980078585\n",
            "Mean of train-time output:  39.31542980078585\n",
            "Mean of test-time output:  39.31542980078585\n",
            "Fraction of train-time output set to zero:  0.6067479848861694\n",
            "Fraction of test-time output set to zero:  0.6067479848861694\n",
            "\n",
            "Running tests with p =  0.7\n",
            "Mean of input:  42.557639883604914\n",
            "Mean of train-time output:  42.557639883604914\n",
            "Mean of test-time output:  42.557639883604914\n",
            "Fraction of train-time output set to zero:  0.701960027217865\n",
            "Fraction of test-time output set to zero:  0.701960027217865\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dt2BpwxswxKn"
      },
      "source": [
        "## Dropout: backward\n",
        "Implement the backward pass for dropout. After doing so, run the following cell to numerically gradient-check your implementation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YEFzMPC7ZuUt"
      },
      "source": [
        "def dropout_backward(dout, cache):\n",
        "  \"\"\"\n",
        "  Perform the backward pass for (inverted) dropout.\n",
        "  Inputs:\n",
        "  - dout: Upstream derivatives, of any shape\n",
        "  - cache: (dropout_param, mask) from Dropout.forward.\n",
        "  \"\"\"\n",
        "  dropout_param, mask = cache\n",
        "  mode = dropout_param['mode']\n",
        "\n",
        "  dx = None\n",
        "  if mode == 'train':\n",
        "    ###########################################################################\n",
        "    # TODO: Implement training phase backward pass for inverted dropout       #\n",
        "    ###########################################################################\n",
        "    # Replace \"pass\" statement with your code\n",
        "    # dx = dout.mm(mask.t().to(dout.dtype))\n",
        "    dx = dout * mask\n",
        "    ###########################################################################\n",
        "    #                            END OF YOUR CODE                             #\n",
        "    ###########################################################################\n",
        "  elif mode == 'test':\n",
        "    dx = dout\n",
        "  return dx\n",
        "\n",
        "Dropout.backward = dropout_backward"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uctLwyIwxKo",
        "outputId": "66c5003a-cd24-4886-c164-dca46962cc54",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "fix_random_seed(0)\n",
        "x = torch.randn(10, 10, **to_double_cuda) + 10\n",
        "dout = torch.randn_like(x)\n",
        "\n",
        "dropout_param = {'mode': 'train', 'p': 0.2, 'seed': 0}\n",
        "out, cache = Dropout.forward(x, dropout_param)\n",
        "dx = Dropout.backward(dout, cache)\n",
        "dx_num = compute_numeric_gradient(lambda xx: Dropout.forward(xx, dropout_param)[0], x, dout)\n",
        "\n",
        "# Error should be around e-10 or less\n",
        "print('dx relative error: ', rel_error(dx, dx_num))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dx relative error:  1.0000000015211765\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OLzMLx-iwxKs"
      },
      "source": [
        "# Fully-connected nets with dropout\n",
        "Modify [your implementation](#scrollTo=7p-goSyucyZH) of `FullyConnectedNet` to use dropout. Specifically, if the constructor of the network receives a value that is not 0 for the `dropout` parameter, then the net should add a dropout layer immediately after every ReLU nonlinearity.\n",
        "\n",
        "After doing so, run the following to numerically gradient-check your implementation. You should see errors less than `1e-5`, and different dropout rates should result different error values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "18ugsX0iwxKu",
        "outputId": "6f51f56f-597e-4aa5-96f7-23df1ec330e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "fix_random_seed(0)\n",
        "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
        "X = torch.randn(N, D, **to_double_cuda)\n",
        "y = torch.randint(C, size=(N,), **to_long_cuda)\n",
        "\n",
        "for dropout in [0, 0.25, 0.5]:\n",
        "  print('Running check with dropout = ', dropout)\n",
        "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
        "                            weight_scale=5e-2, dropout=dropout,\n",
        "                            seed=0, **to_double_cuda)\n",
        "\n",
        "  loss, grads = model.loss(X, y)\n",
        "  print('Initial loss: ', loss.item())\n",
        "  \n",
        "  # Relative errors should be around e-5 or less.\n",
        "  for name in sorted(grads):\n",
        "    f = lambda _: model.loss(X, y)[0]\n",
        "    grad_num = compute_numeric_gradient(f, model.params[name])\n",
        "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
        "  print()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running check with dropout =  0\n",
            "Initial loss:  2.3053575717037686\n",
            "W1 relative error: 7.62e-06\n",
            "W2 relative error: 3.47e-07\n",
            "W3 relative error: 1.39e-07\n",
            "b1 relative error: 4.20e-07\n",
            "b2 relative error: 3.76e-09\n",
            "b3 relative error: 2.34e-10\n",
            "\n",
            "Running check with dropout =  0.25\n",
            "Initial loss:  2.344313155357969\n",
            "W1 relative error: 5.16e-08\n",
            "W2 relative error: 9.55e-09\n",
            "W3 relative error: 1.71e-08\n",
            "b1 relative error: 4.95e-10\n",
            "b2 relative error: 1.70e-09\n",
            "b3 relative error: 3.98e-10\n",
            "\n",
            "Running check with dropout =  0.5\n",
            "Initial loss:  2.3227924981221424\n",
            "W1 relative error: 4.28e-07\n",
            "W2 relative error: 7.70e-08\n",
            "W3 relative error: 1.78e-08\n",
            "b1 relative error: 1.87e-08\n",
            "b2 relative error: 1.50e-09\n",
            "b3 relative error: 2.16e-10\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmhrgg5hwxKy"
      },
      "source": [
        "## Regularization experiment\n",
        "To get a sense of the way that dropout can regularize a neural network, we will train three different two-layer networks:\n",
        "\n",
        "1. Hidden size 256, dropout = 0\n",
        "2. Hidden size 512, dropout = 0\n",
        "3. Hidden size 512, dropout = 0.5\n",
        "\n",
        "We will then visualize the training and validation accuracies of these three networks."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6srh4BLwxKz",
        "outputId": "b2d8daa3-872c-4f01-fde0-13986e8931f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Train two identical nets, one with dropout and one without\n",
        "fix_random_seed(0)\n",
        "num_train = 20000\n",
        "small_data = {\n",
        "  'X_train': data_dict['X_train'][:num_train],\n",
        "  'y_train': data_dict['y_train'][:num_train],\n",
        "  'X_val': data_dict['X_val'],\n",
        "  'y_val': data_dict['y_val'],\n",
        "}\n",
        "\n",
        "solvers = {}\n",
        "dropout_choices = [0, 0, 0.5]\n",
        "width_choices = [256, 512, 512]\n",
        "for dropout, width in zip(dropout_choices, width_choices):\n",
        "# for dropout in dropout_choices:\n",
        "  model = FullyConnectedNet([width], dropout=dropout, **to_float_cuda)\n",
        "  print('Training a model with dropout=%.2f and width=%d' % (dropout, width))\n",
        "\n",
        "  solver = Solver(model, small_data,\n",
        "                  num_epochs=100, batch_size=512,\n",
        "                  update_rule=adam,\n",
        "                  optim_config={\n",
        "                    'learning_rate': 5e-3,\n",
        "                  },\n",
        "                  print_every=100000, print_acc_every=10,\n",
        "                  verbose=True, device='cuda')\n",
        "  solver.train()\n",
        "  solvers[(dropout, width)] = solver\n",
        "  print()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training a model with dropout=0.00 and width=256\n",
            "(Time 0.01 sec; Iteration 1 / 3900) loss: 2.304467\n",
            "(Epoch 0 / 100) train acc: 0.206000; val_acc: 0.200400\n",
            "(Epoch 10 / 100) train acc: 0.690000; val_acc: 0.459600\n",
            "(Epoch 20 / 100) train acc: 0.872000; val_acc: 0.472000\n",
            "(Epoch 30 / 100) train acc: 0.904000; val_acc: 0.475200\n",
            "(Epoch 40 / 100) train acc: 0.963000; val_acc: 0.466400\n",
            "(Epoch 50 / 100) train acc: 0.975000; val_acc: 0.468000\n",
            "(Epoch 60 / 100) train acc: 0.945000; val_acc: 0.458000\n",
            "(Epoch 70 / 100) train acc: 0.993000; val_acc: 0.474000\n",
            "(Epoch 80 / 100) train acc: 0.867000; val_acc: 0.431200\n",
            "(Epoch 90 / 100) train acc: 0.994000; val_acc: 0.477600\n",
            "(Epoch 100 / 100) train acc: 0.983000; val_acc: 0.460000\n",
            "\n",
            "Training a model with dropout=0.00 and width=512\n",
            "(Time 0.01 sec; Iteration 1 / 3900) loss: 2.302591\n",
            "(Epoch 0 / 100) train acc: 0.233000; val_acc: 0.236000\n",
            "(Epoch 10 / 100) train acc: 0.745000; val_acc: 0.480400\n",
            "(Epoch 20 / 100) train acc: 0.889000; val_acc: 0.472000\n",
            "(Epoch 30 / 100) train acc: 0.900000; val_acc: 0.486400\n",
            "(Epoch 40 / 100) train acc: 0.938000; val_acc: 0.473200\n",
            "(Epoch 50 / 100) train acc: 0.962000; val_acc: 0.482800\n",
            "(Epoch 60 / 100) train acc: 0.992000; val_acc: 0.479600\n",
            "(Epoch 70 / 100) train acc: 0.943000; val_acc: 0.464800\n",
            "(Epoch 80 / 100) train acc: 0.991000; val_acc: 0.489200\n",
            "(Epoch 90 / 100) train acc: 0.959000; val_acc: 0.460400\n",
            "(Epoch 100 / 100) train acc: 0.983000; val_acc: 0.474400\n",
            "\n",
            "Training a model with dropout=0.50 and width=512\n",
            "(Time 0.01 sec; Iteration 1 / 3900) loss: 2.302512\n",
            "(Epoch 0 / 100) train acc: 0.219000; val_acc: 0.211600\n",
            "(Epoch 10 / 100) train acc: 0.620000; val_acc: 0.461200\n",
            "(Epoch 20 / 100) train acc: 0.727000; val_acc: 0.484000\n",
            "(Epoch 30 / 100) train acc: 0.848000; val_acc: 0.486400\n",
            "(Epoch 40 / 100) train acc: 0.903000; val_acc: 0.496400\n",
            "(Epoch 50 / 100) train acc: 0.893000; val_acc: 0.501600\n",
            "(Epoch 60 / 100) train acc: 0.911000; val_acc: 0.491600\n",
            "(Epoch 70 / 100) train acc: 0.938000; val_acc: 0.477600\n",
            "(Epoch 80 / 100) train acc: 0.952000; val_acc: 0.496800\n",
            "(Epoch 90 / 100) train acc: 0.941000; val_acc: 0.492800\n",
            "(Epoch 100 / 100) train acc: 0.961000; val_acc: 0.495600\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mjdKh3apRtX4"
      },
      "source": [
        "If everything worked as expected, you should see that the network with dropout has lower training accuracies than the networks without dropout, but that it achieves higher validation accuracies.\n",
        "\n",
        "You should also see that a network with width 512 and dropout 0.5 achieves higher validation accuracies than a network with width 256 and no dropout. This demonstrates that reducing the model size is not generally an effective regularization strategy -- it's often better to use a larger model with explicit regularization."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCDhFCR0wxK2",
        "outputId": "ae8414e7-a168-4513-e66c-053700a85dc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "source": [
        "plt.subplot(3, 1, 1)\n",
        "for (dropout, width), solver in solvers.items():\n",
        "  train_acc = solver.train_acc_history\n",
        "  label = 'dropout=%.2f, width=%d' % (dropout, width)\n",
        "  plt.plot(train_acc, 'o', label=label)\n",
        "plt.title('Train accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(ncol=2, loc='lower right')\n",
        "  \n",
        "plt.subplot(3, 1, 2)\n",
        "for (dropout, width), solver in solvers.items():\n",
        "  val_acc = solver.val_acc_history\n",
        "  label = 'dropout=%.2f, width=%d' % (dropout, width)\n",
        "  plt.plot(val_acc, 'o', label=label)\n",
        "plt.ylim(0.4, 0.52)\n",
        "plt.title('Val accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(ncol=2, loc='lower right')\n",
        "\n",
        "plt.gcf().set_size_inches(15, 15)\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}